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"reactflow__edge-CleanlabEvaluator-Pfyl2{œdataTypeœ:œCleanlabEvaluatorœ,œidœ:œCleanlabEvaluator-Pfyl2œ,œnameœ:œexplanationœ,œoutput_typesœ:[œMessageœ]}-CleanlabRemediator-YB1Ss{œfieldNameœ:œexplanationœ,œidœ:œCleanlabRemediator-YB1Ssœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}", + "selected": false, + "source": "CleanlabEvaluator-Pfyl2", + "sourceHandle": "{œdataTypeœ:œCleanlabEvaluatorœ,œidœ:œCleanlabEvaluator-Pfyl2œ,œnameœ:œexplanationœ,œoutput_typesœ:[œMessageœ]}", + "target": "CleanlabRemediator-YB1Ss", + "targetHandle": "{œfieldNameœ:œexplanationœ,œidœ:œCleanlabRemediator-YB1Ssœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}" + }, + { + "animated": false, + "className": "", + "data": { + "sourceHandle": { + "dataType": "CleanlabEvaluator", + "id": "CleanlabEvaluator-Pfyl2", + "name": "response_passthrough", + "output_types": [ + "Message" + ] + }, + "targetHandle": { + "fieldName": "response", + "id": "CleanlabRemediator-YB1Ss", + "inputTypes": [ + "Message" + ], + "type": "str" + } + }, + "id": "reactflow__edge-CleanlabEvaluator-Pfyl2{œdataTypeœ:œCleanlabEvaluatorœ,œidœ:œCleanlabEvaluator-Pfyl2œ,œnameœ:œresponse_passthroughœ,œoutput_typesœ:[œMessageœ]}-CleanlabRemediator-YB1Ss{œfieldNameœ:œresponseœ,œidœ:œCleanlabRemediator-YB1Ssœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}", + "selected": false, + "source": "CleanlabEvaluator-Pfyl2", + "sourceHandle": "{œdataTypeœ:œCleanlabEvaluatorœ,œidœ:œCleanlabEvaluator-Pfyl2œ,œnameœ:œresponse_passthroughœ,œoutput_typesœ:[œMessageœ]}", + "target": "CleanlabRemediator-YB1Ss", + "targetHandle": "{œfieldNameœ:œresponseœ,œidœ:œCleanlabRemediator-YB1Ssœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}" + } + ], + "nodes": [ + { + "data": { + "description": "Get chat inputs from the Playground.", + "display_name": "Chat Input", + "id": "ChatInput-wk8QV", + "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", + "store_message", + "sender", + "sender_name", + "session_id", + "files" + ], + "frozen": false, + "icon": "MessagesSquare", + "legacy": false, + "lf_version": "1.4.1", + "metadata": {}, + "output_types": [], + "outputs": [ + { + "allows_loop": false, + "cache": true, + "display_name": "Message", + "hidden": false, + "method": "message_response", + "name": "message", + "selected": "Message", + "tool_mode": true, + "types": [ + "Message" + ], + "value": "__UNDEFINED__" + } + ], + "pinned": false, + "template": { + "_type": "Component", + "background_color": { + "_input_type": "MessageTextInput", + "advanced": true, + "display_name": "Background Color", + "dynamic": false, + "info": "The background color of the icon.", + "input_types": [ + "Message" + ], + "list": false, + "load_from_db": false, + "name": "background_color", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + }, + "chat_icon": { + "_input_type": "MessageTextInput", + "advanced": true, + "display_name": "Icon", + "dynamic": false, + "info": "The icon of the message.", + "input_types": [ + "Message" + ], + "list": false, + "load_from_db": false, + "name": "chat_icon", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + }, + "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 langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import (\n DropdownInput,\n FileInput,\n MessageTextInput,\n MultilineInput,\n Output,\n)\nfrom langflow.schema.message import Message\nfrom langflow.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 icon = \"MessagesSquare\"\n name = \"ChatInput\"\n minimized = True\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"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 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 MessageTextInput(\n name=\"background_color\",\n display_name=\"Background Color\",\n info=\"The background color of the icon.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"chat_icon\",\n display_name=\"Icon\",\n info=\"The icon of the message.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"text_color\",\n display_name=\"Text Color\",\n info=\"The text color of the name\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n ]\n\n async def message_response(self) -> Message:\n background_color = self.background_color\n text_color = self.text_color\n icon = self.chat_icon\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 files=self.files,\n properties={\n \"background_color\": background_color,\n \"text_color\": text_color,\n \"icon\": icon,\n },\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" + }, + "files": { + "advanced": true, + "display_name": "Files", + "dynamic": false, + "fileTypes": [ + "txt", + "md", + "mdx", + "csv", + "json", + "yaml", + "yml", + "xml", + "html", + "htm", + "pdf", + "docx", + "py", + "sh", + "sql", + "js", + "ts", + "tsx", + "jpg", + "jpeg", + "png", + "bmp", + "image" + ], + "file_path": "", + "info": "Files to be sent with the message.", + "list": true, + "name": "files", + "placeholder": "", + "required": false, + "show": true, + "temp_file": true, + "title_case": false, + "trace_as_metadata": true, + "type": "file", + "value": "" + }, + "input_value": { + "advanced": false, + "display_name": "Text", + "dynamic": false, + "info": "Message to be passed as input.", + "input_types": [], + "list": false, + "load_from_db": false, + "multiline": true, + "name": "input_value", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "What is the 3rd month of the year alphabetically?" + }, + "sender": { + "advanced": true, + "display_name": "Sender Type", + "dynamic": false, + "info": "Type of sender.", + "name": "sender", + "options": [ + "Machine", + "User" + ], + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "trace_as_metadata": true, + "type": "str", + "value": "User" + }, + "sender_name": { + "advanced": true, + "display_name": "Sender Name", + "dynamic": false, + "info": "Name of the sender.", + "input_types": [ + "Message" + ], + "list": false, + "load_from_db": false, + "name": "sender_name", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "User" + }, + "session_id": { + "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, + "load_from_db": false, + "name": "session_id", + "placeholder": "", + "required": false, + "show": true, + "title_case": 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, + "name": "should_store_message", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "trace_as_metadata": true, + "type": "bool", + "value": true + }, + "text_color": { + "_input_type": "MessageTextInput", + "advanced": true, + "display_name": "Text Color", + "dynamic": false, + "info": "The text color of the name", + "input_types": [ + "Message" + ], + "list": false, + "load_from_db": false, + "name": "text_color", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + } + } + }, + "type": "ChatInput" + }, + "dragging": false, + "height": 234, + "id": "ChatInput-wk8QV", + "measured": { + "height": 234, + "width": 320 + }, + "position": { + "x": 629.0324831254778, + "y": 643.9011650405961 + }, + "positionAbsolute": { + "x": 689.5720422421635, + "y": 765.155834131403 + }, + "selected": false, + "type": "genericNode", + "width": 320 + }, + { + "data": { + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt", + "id": "Prompt-MabiH", + "node": { + "base_classes": [ + "Message" + ], + "beta": false, + "conditional_paths": [], + "custom_fields": { + "template": [] + }, + "description": "Create a system prompt.\n", + "display_name": "System Message", + "documentation": "", + "edited": false, + "error": null, + "field_order": [ + "template" + ], + "frozen": false, + "full_path": null, + "icon": "prompts", + "is_composition": null, + "is_input": null, + "is_output": null, + "legacy": false, + "lf_version": "1.4.1", + "metadata": {}, + "minimized": false, + "name": "", + "output_types": [], + "outputs": [ + { + "allows_loop": false, + "cache": true, + "display_name": "Prompt Message", + "hidden": false, + "method": "build_prompt", + "name": "prompt", + "options": null, + "required_inputs": null, + "selected": "Message", + "tool_mode": true, + "types": [ + "Message" + ], + "value": "__UNDEFINED__" + } + ], + "pinned": false, + "priority": null, + "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 langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n trace_type = \"prompt\"\n name = \"Prompt\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n MessageTextInput(\n name=\"tool_placeholder\",\n display_name=\"Tool Placeholder\",\n tool_mode=True,\n advanced=True,\n info=\"A placeholder input for tool mode.\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n ]\n\n async def build_prompt(self) -> Message:\n prompt = Message.from_template(**self._attributes)\n self.status = prompt.text\n return prompt\n\n def _update_template(self, frontend_node: dict):\n prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n custom_fields = frontend_node[\"custom_fields\"]\n frontend_node_template = frontend_node[\"template\"]\n _ = process_prompt_template(\n template=prompt_template,\n name=\"template\",\n custom_fields=custom_fields,\n frontend_node_template=frontend_node_template,\n )\n return frontend_node\n\n async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):\n \"\"\"This function is called after the code validation is done.\"\"\"\n frontend_node = await super().update_frontend_node(new_frontend_node, current_frontend_node)\n template = frontend_node[\"template\"][\"template\"][\"value\"]\n # Kept it duplicated for backwards compatibility\n _ = process_prompt_template(\n template=template,\n name=\"template\",\n custom_fields=frontend_node[\"custom_fields\"],\n frontend_node_template=frontend_node[\"template\"],\n )\n # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n # and update the frontend_node with those values\n update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n return frontend_node\n\n def _get_fallback_input(self, **kwargs):\n return DefaultPromptField(**kwargs)\n" + }, + "template": { + "_input_type": "PromptInput", + "advanced": false, + "display_name": "Template", + "dynamic": false, + "info": "", + "list": false, + "load_from_db": false, + "name": "template", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "type": "prompt", + "value": "You are a helpful assistant.\n" + }, + "tool_placeholder": { + "_input_type": "MessageTextInput", + "advanced": true, + "display_name": "Tool Placeholder", + "dynamic": false, + "info": "A placeholder input for tool mode.", + "input_types": [ + "Message" + ], + "list": false, + "load_from_db": false, + "name": "tool_placeholder", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": true, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + } + }, + "tool_mode": false + }, + "type": "Prompt" + }, + "dragging": false, + "height": 260, + "id": "Prompt-MabiH", + "measured": { + "height": 260, + "width": 320 + }, + "position": { + "x": 624.7914648802054, + "y": 958.3921915653085 + }, + "positionAbsolute": { + "x": 690.2015147036818, + "y": 1018.5443911764344 + }, + "selected": false, + "type": "genericNode", + "width": 320 + }, + { + "data": { + "id": "OpenAIModel-1P8nP", + "node": { + "base_classes": [ + "LanguageModel", + "Message" + ], + "beta": false, + "conditional_paths": [], + "custom_fields": {}, + "description": "Generates text using OpenAI LLMs.", + "display_name": "OpenAI", + "documentation": "", + "edited": false, + "field_order": [ + "input_value", + "system_message", + "stream", + "max_tokens", + "model_kwargs", + "json_mode", + "model_name", + "openai_api_base", + "api_key", + "temperature", + "seed", + "max_retries", + "timeout" + ], + "frozen": false, + "icon": "OpenAI", + "legacy": false, + "lf_version": "1.4.1", + "metadata": {}, + "minimized": false, + "output_types": [], + "outputs": [ + { + "allows_loop": false, + "cache": true, + "display_name": "Message", + "hidden": false, + "method": "text_response", + "name": "text_output", + "required_inputs": [], + "selected": "Message", + "tool_mode": true, + "types": [ + "Message" + ], + "value": "__UNDEFINED__" + }, + { + "allows_loop": false, + "cache": true, + "display_name": "Language Model", + "method": "build_model", + "name": "model_output", + "required_inputs": [ + "api_key" + ], + "selected": "LanguageModel", + "tool_mode": true, + "types": [ + "LanguageModel" + ], + "value": "__UNDEFINED__" + } + ], + "pinned": false, + "template": { + "_type": "Component", + "api_key": { + "_input_type": "SecretStrInput", + "advanced": false, + "display_name": "OpenAI API Key", + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "input_types": [], + "load_from_db": false, + "name": "api_key", + "password": true, + "placeholder": "", + "required": true, + "show": true, + "title_case": false, + "type": "str", + "value": "" + }, + "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\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import (\n OPENAI_MODEL_NAMES,\n OPENAI_REASONING_MODEL_NAMES,\n)\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.logging import logger\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n show=True,\n ),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n parameters = {\n \"api_key\": SecretStr(self.api_key).get_secret_value() if self.api_key else None,\n \"model_name\": self.model_name,\n \"max_tokens\": self.max_tokens or None,\n \"model_kwargs\": self.model_kwargs or {},\n \"base_url\": self.openai_api_base or \"https://api.openai.com/v1\",\n \"seed\": self.seed,\n \"max_retries\": self.max_retries,\n \"timeout\": self.timeout,\n \"temperature\": self.temperature if self.temperature is not None else 0.1,\n }\n\n logger.info(f\"Model name: {self.model_name}\")\n if self.model_name in OPENAI_REASONING_MODEL_NAMES:\n logger.info(\"Getting reasoning model parameters\")\n parameters.pop(\"temperature\")\n parameters.pop(\"seed\")\n output = ChatOpenAI(**parameters)\n if self.json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\")\n if message:\n return message\n return None\n\n def update_build_config(self, build_config: dict, field_value: Any, field_name: str | None = None) -> dict:\n if field_name in {\"base_url\", \"model_name\", \"api_key\"} and field_value in OPENAI_REASONING_MODEL_NAMES:\n build_config[\"temperature\"][\"show\"] = False\n build_config[\"seed\"][\"show\"] = False\n if field_name in {\"base_url\", \"model_name\", \"api_key\"} and field_value in OPENAI_MODEL_NAMES:\n build_config[\"temperature\"][\"show\"] = True\n build_config[\"seed\"][\"show\"] = True\n return build_config\n" + }, + "input_value": { + "_input_type": "MessageInput", + "advanced": false, + "display_name": "Input", + "dynamic": false, + "info": "", + "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": "" + }, + "json_mode": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "JSON Mode", + "dynamic": false, + "info": "If True, it will output JSON regardless of passing a schema.", + "list": false, + "list_add_label": "Add More", + "name": "json_mode", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "bool", + "value": false + }, + "max_retries": { + "_input_type": "IntInput", + "advanced": true, + "display_name": "Max Retries", + "dynamic": false, + "info": "The maximum number of retries to make when generating.", + "list": false, + "list_add_label": "Add More", + "name": "max_retries", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "int", + "value": 5 + }, + "max_tokens": { + "_input_type": "IntInput", + "advanced": true, + "display_name": "Max Tokens", + "dynamic": false, + "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", + "list": false, + "list_add_label": "Add More", + "name": "max_tokens", + "placeholder": "", + "range_spec": { + "max": 128000, + "min": 0, + "step": 0.1, + "step_type": "float" + }, + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "int", + "value": "" + }, + "model_kwargs": { + "_input_type": "DictInput", + "advanced": true, + "display_name": "Model Kwargs", + "dynamic": false, + "info": "Additional keyword arguments to pass to the model.", + "list": false, + "list_add_label": "Add More", + "name": "model_kwargs", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "type": "dict", + "value": {} + }, + "model_name": { + "_input_type": "DropdownInput", + "advanced": false, + "combobox": true, + "dialog_inputs": {}, + "display_name": "Model Name", + "dynamic": false, + "info": "", + "name": "model_name", + "options": [ + "gpt-4o-mini", + "gpt-4o", + "gpt-4.1", + "gpt-4.1-mini", + "gpt-4.1-nano", + "gpt-4.5-preview", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-4", + "gpt-3.5-turbo", + "o1" + ], + "options_metadata": [], + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "str", + "value": "gpt-4o" + }, + "openai_api_base": { + "_input_type": "StrInput", + "advanced": true, + "display_name": "OpenAI API Base", + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.", + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "openai_api_base", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "str", + "value": "" + }, + "seed": { + "_input_type": "IntInput", + "advanced": true, + "display_name": "Seed", + "dynamic": false, + "info": "The seed controls the reproducibility of the job.", + "list": false, + "list_add_label": "Add More", + "name": "seed", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "int", + "value": 1 + }, + "stream": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "Stream", + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "list": false, + "list_add_label": "Add More", + "name": "stream", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "bool", + "value": false + }, + "system_message": { + "_input_type": "MultilineInput", + "advanced": false, + "display_name": "System Message", + "dynamic": false, + "info": "System message to pass to the model.", + "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": "" + }, + "temperature": { + "_input_type": "SliderInput", + "advanced": false, + "display_name": "Temperature", + "dynamic": false, + "info": "", + "load_from_db": false, + "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": 0.2 + }, + "timeout": { + "_input_type": "IntInput", + "advanced": true, + "display_name": "Timeout", + "dynamic": false, + "info": "The timeout for requests to OpenAI completion API.", + "list": false, + "list_add_label": "Add More", + "name": "timeout", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "int", + "value": 700 + } + }, + "tool_mode": false + }, + "showNode": true, + "type": "OpenAIModel" + }, + "dragging": false, + "id": "OpenAIModel-1P8nP", + "measured": { + "height": 614, + "width": 320 + }, + "position": { + "x": 1070.7203298910767, + "y": 624.1515018284463 + }, + "selected": false, + "type": "genericNode" + }, + { + "data": { + "id": "CleanlabEvaluator-Pfyl2", + "node": { + "base_classes": [ + "float", + "Message", + "number" + ], + "beta": false, + "conditional_paths": [], + "custom_fields": {}, + "description": "Evaluates any LLM response using Cleanlab and outputs trust score and explanation.", + "display_name": "Cleanlab Evaluator", + "documentation": "", + "edited": false, + "field_order": [ + "system_prompt", + "prompt", + "response", + "api_key", + "model", + "quality_preset" + ], + "frozen": false, + "icon": "Cleanlab", + "legacy": false, + "lf_version": "1.4.1", + "metadata": {}, + "minimized": false, + "output_types": [], + "outputs": [ + { + "allows_loop": false, + "cache": true, + "display_name": "Response", + "hidden": false, + "method": "pass_response", + "name": "response_passthrough", + "options": null, + "required_inputs": null, + "selected": "Message", + "tool_mode": true, + "types": [ + "Message" + ], + "value": "__UNDEFINED__" + }, + { + "allows_loop": false, + "cache": true, + "display_name": "Trust Score", + "hidden": false, + "method": "get_score", + "name": "score", + "options": null, + "required_inputs": null, + "selected": "number", + "tool_mode": true, + "types": [ + "number", + "float" + ], + "value": "__UNDEFINED__" + }, + { + "allows_loop": false, + "cache": true, + "display_name": "Explanation", + "hidden": false, + "method": "get_explanation", + "name": "explanation", + "options": null, + "required_inputs": null, + "selected": "Message", + "tool_mode": true, + "types": [ + "Message" + ], + "value": "__UNDEFINED__" + } + ], + "pinned": false, + "template": { + "_type": "Component", + "api_key": { + "_input_type": "SecretStrInput", + "advanced": false, + "display_name": "Cleanlab API Key", + "dynamic": false, + "info": "Your Cleanlab API key.", + "input_types": [], + "load_from_db": false, + "name": "api_key", + "password": true, + "placeholder": "", + "required": true, + "show": true, + "title_case": false, + "type": "str", + "value": "" + }, + "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 langflow.custom import Component\nfrom langflow.io import (\n MessageTextInput,\n Output,\n SecretStrInput,\n DropdownInput,\n)\nfrom langflow.schema.message import Message\nfrom cleanlab_tlm import TLM\n\n\nclass CleanlabEvaluator(Component):\n \"\"\"\n A component that evaluates the trustworthiness of LLM responses using Cleanlab.\n\n This component takes a prompt and response pair, along with optional system instructions,\n and uses Cleanlab's evaluation algorithms to generate a trust score and explanation.\n\n Inputs:\n - system_prompt (MessageTextInput): Optional system-level instructions prepended to the user prompt.\n - prompt (MessageTextInput): The user's prompt or query sent to the LLM.\n - response (MessageTextInput): The response generated by the LLM to be evaluated. This should come from the LLM component, i.e. OpenAI, Gemini, etc.\n - api_key (SecretStrInput): Your Cleanlab API key.\n - model (DropdownInput): The model used by Cleanlab to evaluate the response (can differ from the generation model).\n - quality_preset (DropdownInput): Tradeoff setting for accuracy vs. speed and cost. Higher presets are slower but more accurate.\n\n Outputs:\n - response_passthrough (Message): The original response, passed through for downstream use.\n - score (number): A float between 0 and 1 indicating Cleanlab's trustworthiness score for the response.\n - explanation (Message): A textual explanation of why the response received its score.\n\n This component works well in conjunction with the CleanlabRemediator to create a complete trust evaluation and remediation pipeline.\n\n More details on the evaluation metrics can be found here: https://help.cleanlab.ai/tlm/tutorials/tlm/\n \"\"\"\n\n display_name = \"Cleanlab Evaluator\"\n description = \"Evaluates any LLM response using Cleanlab and outputs trust score and explanation.\"\n icon = \"Cleanlab\"\n name = \"CleanlabEvaluator\"\n\n inputs = [\n MessageTextInput(\n name=\"system_prompt\",\n display_name=\"System Message\",\n info=\"System-level instructions prepended to the user query.\",\n value=\"\",\n ),\n MessageTextInput(\n name=\"prompt\",\n display_name=\"Prompt\",\n info=\"The user's query to the model.\",\n required=True,\n ),\n MessageTextInput(\n name=\"response\",\n display_name=\"Response\",\n info=\"The response to the user's query.\",\n required=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Cleanlab API Key\",\n info=\"Your Cleanlab API key.\",\n required=True,\n ),\n DropdownInput(\n name=\"model\",\n display_name=\"Cleanlab Evaluation Model\",\n options=[\n \"gpt-4.1\", \"gpt-4.1-mini\", \"gpt-4.1-nano\", \"o4-mini\", \"o3\",\n \"gpt-4.5-preview\", \"gpt-4o-mini\", \"gpt-4o\", \"o3-mini\", \"o1\", \"o1-mini\",\n \"gpt-4\", \"gpt-3.5-turbo-16k\",\n \"claude-3.7-sonnet\", \"claude-3.5-sonnet-v2\", \"claude-3.5-sonnet\",\n \"claude-3.5-haiku\", \"claude-3-haiku\",\n \"nova-micro\", \"nova-lite\", \"nova-pro\"\n ],\n info=\"The model Cleanlab uses to evaluate the response. This does NOT need to be the same model that generated the response.\",\n value=\"gpt-4o-mini\",\n required=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"quality_preset\",\n display_name=\"Quality Preset\",\n options=[\"base\", \"low\", \"medium\", \"high\", \"best\"],\n value=\"medium\",\n info=\"This determines the accuracy, latency, and cost of the evaluation. Higher quality is generally slower but more accurate.\",\n required=True,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Response\", name=\"response_passthrough\", method=\"pass_response\", types=[\"Message\"]),\n Output(display_name=\"Trust Score\", name=\"score\", method=\"get_score\", types=[\"number\"]),\n Output(display_name=\"Explanation\", name=\"explanation\", method=\"get_explanation\", types=[\"Message\"]),\n ]\n\n def _evaluate_once(self):\n if not hasattr(self, \"_cached_result\"):\n full_prompt = f\"{self.system_prompt}\\n\\n{self.prompt}\" if self.system_prompt else self.prompt\n tlm = TLM(\n api_key=self.api_key,\n options={\"log\": [\"explanation\"], \"model\": self.model},\n quality_preset=self.quality_preset,\n )\n self._cached_result = tlm.get_trustworthiness_score(full_prompt, self.response)\n return self._cached_result\n\n def get_score(self) -> float:\n result = self._evaluate_once()\n score = result.get(\"trustworthiness_score\", 0.0)\n self.status = f\"Trust score: {score:.2f}\"\n return score\n\n def get_explanation(self) -> Message:\n result = self._evaluate_once()\n explanation = result.get(\"log\", {}).get(\"explanation\", \"No explanation returned.\")\n return Message(text=explanation)\n \n def pass_response(self) -> Message:\n self.status = \"Passing through response.\"\n return Message(text=self.response)" + }, + "model": { + "_input_type": "DropdownInput", + "advanced": true, + "combobox": false, + "dialog_inputs": {}, + "display_name": "Cleanlab Evaluation Model", + "dynamic": false, + "info": "The model Cleanlab uses to evaluate the response. This does NOT need to be the same model that generated the response.", + "name": "model", + "options": [ + "gpt-4.1", + "gpt-4.1-mini", + "gpt-4.1-nano", + "o4-mini", + "o3", + "gpt-4.5-preview", + "gpt-4o-mini", + "gpt-4o", + "o3-mini", + "o1", + "o1-mini", + "gpt-4", + "gpt-3.5-turbo-16k", + "claude-3.7-sonnet", + "claude-3.5-sonnet-v2", + "claude-3.5-sonnet", + "claude-3.5-haiku", + "claude-3-haiku", + "nova-micro", + "nova-lite", + "nova-pro" + ], + "options_metadata": [], + "placeholder": "", + "required": true, + "show": true, + "title_case": false, + "toggle": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "str", + "value": "gpt-4o-mini" + }, + "prompt": { + "_input_type": "MessageTextInput", + "advanced": false, + "display_name": "Prompt", + "dynamic": false, + "info": "The user's query to the model.", + "input_types": [ + "Message" + ], + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "prompt", + "placeholder": "", + "required": true, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + }, + "quality_preset": { + "_input_type": "DropdownInput", + "advanced": true, + "combobox": false, + "dialog_inputs": {}, + "display_name": "Quality Preset", + "dynamic": false, + "info": "This determines the accuracy, latency, and cost of the evaluation. Higher quality is generally slower but more accurate.", + "name": "quality_preset", + "options": [ + "base", + "low", + "medium", + "high", + "best" + ], + "options_metadata": [], + "placeholder": "", + "required": true, + "show": true, + "title_case": false, + "toggle": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "str", + "value": "medium" + }, + "response": { + "_input_type": "MessageTextInput", + "advanced": false, + "display_name": "Response", + "dynamic": false, + "info": "The response to the user's query.", + "input_types": [ + "Message" + ], + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "response", + "placeholder": "", + "required": true, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + }, + "system_prompt": { + "_input_type": "MessageTextInput", + "advanced": false, + "display_name": "System Message", + "dynamic": false, + "info": "System-level instructions prepended to the user query.", + "input_types": [ + "Message" + ], + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "system_prompt", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + } + }, + "tool_mode": false + }, + "showNode": true, + "type": "CleanlabEvaluator" + }, + "dragging": false, + "id": "CleanlabEvaluator-Pfyl2", + "measured": { + "height": 593, + "width": 320 + }, + "position": { + "x": 1471.9658869078787, + "y": 640.3964922751719 + }, + "selected": false, + "type": "genericNode" + }, + { + "data": { + "id": "CleanlabRemediator-YB1Ss", + "node": { + "base_classes": [ + "Message" + ], + "beta": false, + "conditional_paths": [], + "custom_fields": {}, + "description": "Remediates an untrustworthy response based on trust score from the Cleanlab Evaluator, score threshold, and message handling settings.", + "display_name": "Cleanlab Remediator", + "documentation": "", + "edited": true, + "field_order": [ + "response", + "score", + "explanation", + "threshold", + "show_untrustworthy_response", + "untrustworthy_warning_text", + "fallback_text" + ], + "frozen": false, + "icon": "Cleanlab", + "legacy": false, + "lf_version": "1.4.1", + "metadata": {}, + "minimized": false, + "output_types": [], + "outputs": [ + { + "allows_loop": false, + "cache": true, + "display_name": "Remediated Message", + "hidden": false, + "method": "remediate_response", + "name": "remediated_response", + "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 langflow.custom import Component\nfrom langflow.io import (\n MessageTextInput,\n PromptInput,\n Output,\n BoolInput,\n HandleInput,\n FloatInput\n)\nfrom langflow.template import Input\nfrom langflow.schema.message import Message\nfrom langflow.field_typing.range_spec import RangeSpec\n\n\nclass CleanlabRemediator(Component):\n \"\"\"\n A component that remediates potentially untrustworthy LLM responses based on trust scores computed by the Cleanlab Evaluator.\n\n This component takes a response and its associated trust score,\n and applies remediation strategies based on configurable thresholds and settings.\n\n Inputs:\n - response (MessageTextInput): The original LLM-generated response to be evaluated and possibly remediated. The CleanlabEvaluator passes this response through.\n - score (HandleInput): The trust score output from CleanlabEvaluator (expected to be a float between 0 and 1).\n - explanation (MessageTextInput): Optional textual explanation for the trust score, to be included in the output.\n - threshold (Input[float]): Minimum trust score required to accept the response. If the score is lower, the response is remediated.\n - show_untrustworthy_response (BoolInput): If true, returns the original response with a warning; if false, returns fallback text.\n - untrustworthy_warning_text (PromptInput): Text warning to append to responses deemed untrustworthy (when showing them).\n - fallback_text (PromptInput): Replacement message returned if the response is untrustworthy and should be hidden.\n\n Outputs:\n - remediated_response (Message): Either:\n • the original response,\n • the original response with appended warning, or\n • the fallback response,\n depending on the trust score and configuration.\n\n This component is typically used downstream of CleanlabEvaluator or CleanlabRagValidator\n to take appropriate action on low-trust responses and inform users accordingly.\n \"\"\"\n \n display_name = \"Cleanlab Remediator\"\n description = \"Remediates an untrustworthy response based on trust score from the Cleanlab Evaluator, score threshold, and message handling settings.\"\n icon = \"Cleanlab\"\n name = \"CleanlabRemediator\"\n\n inputs = [\n MessageTextInput(\n name=\"response\",\n display_name=\"Response\",\n info=\"The response to the user's query.\",\n required=True,\n ),\n HandleInput(\n name=\"score\",\n display_name=\"Trust Score\",\n info=\"The trustworthiness score output from the Cleanlab Evaluator.\",\n input_types=[\"number\"],\n required=True,\n ),\n MessageTextInput(\n name=\"explanation\",\n display_name=\"Explanation\",\n info=\"The explanation from the Cleanlab Evaluator.\",\n required=False,\n ),\n FloatInput(\n name=\"threshold\",\n display_name=\"Threshold\",\n field_type=\"float\",\n value=0.7,\n range_spec=RangeSpec(min=0.0, max=1.0, step=0.05),\n info=\"Minimum score required to show the response unmodified. Reponses with scores above this threshold are considered trustworthy. Reponses with scores below this threshold are considered untrustworthy and will be remediated based on the settings below.\",\n required=True,\n show=True,\n ),\n BoolInput(\n name=\"show_untrustworthy_response\",\n display_name=\"Show Untrustworthy Response\",\n info=\"If enabled, and the trust score is below the threshold, the original response is shown with the added warning. If disabled, and the trust score is below the threshold, the fallback answer is returned.\",\n value=True,\n ),\n PromptInput(\n name=\"untrustworthy_warning_text\",\n display_name=\"Warning for Untrustworthy Response\",\n info=\"Warning to append to the response if Show Untrustworthy Response is enabled and trust score is below the threshold.\",\n value=\"⚠️ WARNING: The following response is potentially untrustworthy.\",\n ),\n PromptInput(\n name=\"fallback_text\",\n display_name=\"Fallback Answer\",\n info=\"Response returned if the trust score is below the threshold and 'Show Untrustworthy Response' is disabled.\",\n value=\"Based on the available information, I cannot provide a complete answer to this question.\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Remediated Message\", name=\"remediated_response\", method=\"remediate_response\", types=[\"Message\"]),\n ]\n \n def remediate_response(self) -> Message:\n if self.score >= self.threshold:\n self.status = f\"Score {self.score:.2f} ≥ threshold {self.threshold:.2f} → accepted\"\n return Message(\n text=f\"{self.response}\\n\\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\n\\n**Trust Score:** {self.score:.2f}\"\n )\n \n self.status = f\"Score {self.score:.2f} < threshold {self.threshold:.2f} → flagged\"\n \n if self.show_untrustworthy_response:\n parts = [\n self.response,\n \"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\",\n f\"**{self.untrustworthy_warning_text.strip()}**\",\n f\"**Trust Score:** {self.score:.2f}\",\n ]\n if self.explanation:\n parts.append(f\"**Explanation:** {self.explanation}\")\n return Message(text=\"\\n\\n\".join(parts))\n \n return Message(text=self.fallback_text)\n" + }, + "explanation": { + "_input_type": "MessageTextInput", + "advanced": false, + "display_name": "Explanation", + "dynamic": false, + "info": "The explanation from the Cleanlab Evaluator.", + "input_types": [ + "Message" + ], + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "explanation", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + }, + "fallback_text": { + "_input_type": "PromptInput", + "advanced": false, + "display_name": "Fallback Answer", + "dynamic": false, + "info": "Response returned if the trust score is below the threshold and 'Show Untrustworthy Response' is disabled.", + "list": false, + "list_add_label": "Add More", + "name": "fallback_text", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "type": "prompt", + "value": "Based on the available information, I cannot provide a complete answer to this question." + }, + "response": { + "_input_type": "MessageTextInput", + "advanced": false, + "display_name": "Response", + "dynamic": false, + "info": "The response to the user's query.", + "input_types": [ + "Message" + ], + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "response", + "placeholder": "", + "required": true, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + }, + "score": { + "_input_type": "HandleInput", + "advanced": false, + "display_name": "Trust Score", + "dynamic": false, + "info": "The trustworthiness score output from the Cleanlab Evaluator.", + "input_types": [ + "number" + ], + "list": false, + "list_add_label": "Add More", + "name": "score", + "placeholder": "", + "required": true, + "show": true, + "title_case": false, + "trace_as_metadata": true, + "type": "other", + "value": "" + }, + "show_untrustworthy_response": { + "_input_type": "BoolInput", + "advanced": false, + "display_name": "Show Untrustworthy Response", + "dynamic": false, + "info": "If enabled, and the trust score is below the threshold, the original response is shown with the added warning. If disabled, and the trust score is below the threshold, the fallback answer is returned.", + "list": false, + "list_add_label": "Add More", + "name": "show_untrustworthy_response", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "bool", + "value": true + }, + "threshold": { + "_input_type": "FloatInput", + "advanced": false, + "display_name": "Threshold", + "dynamic": false, + "info": "Minimum score required to show the response unmodified. Reponses with scores above this threshold are considered trustworthy. Reponses with scores below this threshold are considered untrustworthy and will be remediated based on the settings below.", + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "threshold", + "placeholder": "", + "range_spec": { + "max": 1, + "min": 0, + "step": 0.05, + "step_type": "float" + }, + "required": true, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "float", + "value": 0.7 + }, + "untrustworthy_warning_text": { + "_input_type": "PromptInput", + "advanced": false, + "display_name": "Warning for Untrustworthy Response", + "dynamic": false, + "info": "Warning to append to the response if Show Untrustworthy Response is enabled and trust score is below the threshold.", + "list": false, + "list_add_label": "Add More", + "name": "untrustworthy_warning_text", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "type": "prompt", + "value": "⚠️ WARNING: The following response is potentially untrustworthy." + } + }, + "tool_mode": false + }, + "showNode": true, + "type": "CleanlabRemediator" + }, + "dragging": false, + "id": "CleanlabRemediator-YB1Ss", + "measured": { + "height": 744, + "width": 320 + }, + "position": { + "x": 1901.0079628887015, + "y": 571.0103984031506 + }, + "selected": true, + "type": "genericNode" + }, + { + "data": { + "id": "ChatOutput-9jsnO", + "node": { + "base_classes": [ + "Message" + ], + "beta": false, + "category": "outputs", + "conditional_paths": [], + "custom_fields": {}, + "description": "Display a chat message in the Playground.", + "display_name": "Chat Output", + "documentation": "", + "edited": false, + "field_order": [ + "input_value", + "should_store_message", + "sender", + "sender_name", + "session_id", + "data_template", + "background_color", + "chat_icon", + "text_color", + "clean_data" + ], + "frozen": false, + "icon": "MessagesSquare", + "key": "ChatOutput", + "legacy": false, + "lf_version": "1.4.1", + "metadata": {}, + "minimized": true, + "output_types": [], + "outputs": [ + { + "allows_loop": false, + "cache": true, + "display_name": "Message", + "method": "message_response", + "name": "message", + "selected": "Message", + "tool_mode": true, + "types": [ + "Message" + ], + "value": "__UNDEFINED__" + } + ], + "pinned": false, + "score": 0.003169567463043492, + "template": { + "_type": "Component", + "background_color": { + "_input_type": "MessageTextInput", + "advanced": true, + "display_name": "Background Color", + "dynamic": false, + "info": "The background color of the icon.", + "input_types": [ + "Message" + ], + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "background_color", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + }, + "chat_icon": { + "_input_type": "MessageTextInput", + "advanced": true, + "display_name": "Icon", + "dynamic": false, + "info": "The icon of the message.", + "input_types": [ + "Message" + ], + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "chat_icon", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + }, + "clean_data": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "Basic Clean Data", + "dynamic": false, + "info": "Whether to clean the data", + "list": false, + "list_add_label": "Add More", + "name": "clean_data", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "bool", + "value": true + }, + "code": { + "advanced": true, + "dynamic": true, + "fileTypes": [], + "file_path": "", + "info": "", + "list": false, + "load_from_db": false, + "multiline": true, + "name": "code", + "password": false, + "placeholder": "", + "required": true, + "show": true, + "title_case": false, + "type": "code", + "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.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 icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Text\",\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=\"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 MessageTextInput(\n name=\"background_color\",\n display_name=\"Background Color\",\n info=\"The background color of the icon.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"chat_icon\",\n display_name=\"Icon\",\n info=\"The icon of the message.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"text_color\",\n display_name=\"Text Color\",\n info=\"The text color of the name\",\n advanced=True,\n ),\n BoolInput(\n name=\"clean_data\",\n display_name=\"Basic Clean Data\",\n value=True,\n info=\"Whether to clean the data\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(\n display_name=\"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 # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n background_color = self.background_color\n text_color = self.text_color\n if self.chat_icon:\n icon = self.chat_icon\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.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 message.properties.icon = icon\n message.properties.background_color = background_color\n message.properties.text_color = text_color\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 _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 _safe_convert(self, data: Any) -> str:\n \"\"\"Safely convert input data to string.\"\"\"\n try:\n if isinstance(data, str):\n return data\n if isinstance(data, Message):\n return data.get_text()\n if isinstance(data, Data):\n if data.get_text() is None:\n msg = \"Empty Data object\"\n raise ValueError(msg)\n return data.get_text()\n if isinstance(data, DataFrame):\n if self.clean_data:\n # Remove empty rows\n data = data.dropna(how=\"all\")\n # Remove empty lines in each cell\n data = data.replace(r\"^\\s*$\", \"\", regex=True)\n # Replace multiple newlines with a single newline\n data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n\n # Replace pipe characters to avoid markdown table issues\n processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n processed_data = processed_data.map(\n lambda x: str(x).replace(\"\\n\", \"
\") if isinstance(x, str) else x\n )\n\n return processed_data.to_markdown(index=False)\n return str(data)\n except (ValueError, TypeError, AttributeError) as e:\n msg = f\"Error converting data: {e!s}\"\n raise ValueError(msg) from e\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 return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return self._safe_convert(self.input_value)\n" + }, + "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": "Text", + "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 + }, + "text_color": { + "_input_type": "MessageTextInput", + "advanced": true, + "display_name": "Text Color", + "dynamic": false, + "info": "The text color of the name", + "input_types": [ + "Message" + ], + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "text_color", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + } + }, + "tool_mode": false + }, + "showNode": false, + "type": "ChatOutput" + }, + "dragging": false, + "id": "ChatOutput-9jsnO", + "measured": { + "height": 66, + "width": 192 + }, + "position": { + "x": 2471.3143292418977, + "y": 832.7888356096034 + }, + "selected": false, + "type": "genericNode" + } + ], + "viewport": { + "x": -43.419583305059405, + "y": -25.79876409873117, + "zoom": 0.581082931088503 + } + }, + "description": "Perform basic prompting with an OpenAI model.", + "endpoint_name": null, + "id": "f3c3b56c-f5f6-44aa-9c13-67447e55a652", + "is_component": false, + "last_tested_version": "1.4.1", + "name": "Cleanlab Evaluator and Remediator", + "tags": [ + "chatbots" + ] +} \ No newline at end of file diff --git a/docs/docs/Integrations/Cleanlab/eval_rag.png b/docs/docs/Integrations/Cleanlab/eval_rag.png new file mode 100644 index 000000000000..bd84c9800a81 Binary files /dev/null and b/docs/docs/Integrations/Cleanlab/eval_rag.png differ diff --git a/docs/docs/Integrations/Cleanlab/eval_response.png b/docs/docs/Integrations/Cleanlab/eval_response.png new file mode 100644 index 000000000000..ec08ec54ac7d Binary files /dev/null and b/docs/docs/Integrations/Cleanlab/eval_response.png differ diff --git a/docs/docs/Integrations/Cleanlab/eval_summary_rag.png b/docs/docs/Integrations/Cleanlab/eval_summary_rag.png new file mode 100644 index 000000000000..6a292679434e Binary files /dev/null and b/docs/docs/Integrations/Cleanlab/eval_summary_rag.png differ diff --git a/docs/docs/Integrations/Cleanlab/integrations-cleanlab.md b/docs/docs/Integrations/Cleanlab/integrations-cleanlab.md new file mode 100644 index 000000000000..41add78e441b --- /dev/null +++ b/docs/docs/Integrations/Cleanlab/integrations-cleanlab.md @@ -0,0 +1,145 @@ +--- +title: Integrate Cleanlab Evaluations with Langflow +slug: /integrations-cleanlab +--- + +Unlock trustworthy Agentic, RAG, and LLM pipelines with Cleanlab's evaluation and remediation suite. + +[Cleanlab](https://www.cleanlab.ai/) adds automation and trust to every data point going in and every prediction coming out of AI and RAG solutions. + +This Langflow integration provides 3 modular components that assess and improve the **trustworthiness** of any LLM or RAG pipeline output, enabling critical oversight for safety-sensitive, enterprise, and production GenAI applications. + +Use this bundle to: +- Quantify trustworthiness of ANY LLM response with a **0-1 score** +- Explain why a response may be good or bad +- Evaluate **context sufficiency**, **groundedness**, **helpfulness**, and **query clarity** with quantitative scores (for RAG/Agentic pipelines with context) +- Remediate low-trust responses with warnings or fallback answers + +## Prerequisites + +Before using these components, you'll need: + +- A [Cleanlab API key](https://tlm.cleanlab.ai/) + + +## Components + +### `CleanlabEvaluator` + +**Purpose:** Evaluate and explain the trustworthiness of a prompt + response pair using Cleanlab. More details on how the score works [here](https://help.cleanlab.ai/tlm/). + +#### Inputs + +| Name | Type | Description | +|-----------------------|------------|---------------------------------------------------------------------| +| system_prompt | Message | (Optional) System message prepended to the prompt | +| prompt | Message | The user-facing input to the LLM | +| response | Message | OpenAI's, Claude, etc. model's response to evaluate | +| cleanlab_api_key | Secret | Your Cleanlab API key | +| cleanlab_evaluation_model | Dropdown | Evaluation model used by Cleanlab (GPT-4, Claude, etc.) This does not need to be the same model that generated the response. | +| quality_preset | Dropdown | Tradeoff between evaluation speed and accuracy | + +#### Outputs + +| Name | Type | Description | +|-----------------------|------------|---------------------------------------------------------------------| +| score | number | Trust score between 0–1 | +| explanation | Message | Explanation of the trust score | +| response | Message | Returns the original response for easy chaining to `CleanlabRemediator` component | + +--- + +### `CleanlabRemediator` + +**Purpose:** Use the trust score from the `CleanlabEvaluator` component to determine whether to show, warn about, or replace an LLM response. This component has configurables for the score threshold, warning text, and fallback message which you can customize as needed. + +#### Inputs + +| Name | Type | Description | +|-----------------------------|------------|-----------------------------------------------------------------------------| +| response | Message | The response to potentially remediate | +| score | number | Trust score from `CleanlabEvaluator` | +| explanation | Message | (Optional) Explanation to append if warning is shown | +| threshold | float | Minimum trust score to pass response unchanged | +| show_untrustworthy_response| bool | Show original response with warning if untrustworthy | +| untrustworthy_warning_text | Prompt | Warning text for untrustworthy responses | +| fallback_text | Prompt | Fallback message if response is hidden | + +#### Output + +| Name | Type | Description | +|-----------------------|------------|-----------------------------------------------------------------------------| +| remediated_response | Message | Final message shown to user after remediation logic | + + +See example outputs below! + +--- + +### `CleanlabRAGEvaluator` + +**Purpose:** Comprehensively evaluate RAG and LLM pipeline outputs by analyzing the context, query, and response quality using Cleanlab. This component assesses trustworthiness, context sufficiency, response groundedness, helpfulness, and query ease. Learn more about Cleanlab's evaluation metrics [here](https://help.cleanlab.ai/tlm/use-cases/tlm_rag/). You can also use the `CleanlabRemediator` component with this one to remediate low-trust responses coming from the RAG pipeline. + +#### Inputs + +| Name | Type | Description | +|--------------------------|-----------|----------------------------------------------------------------------------| +| cleanlab_api_key | Secret | Your Cleanlab API key | +| cleanlab_evaluation_model | Dropdown | Evaluation model used by Cleanlab (GPT-4, Claude, etc.) This does not need to be the same model that generated the response. | +| quality_preset | Dropdown | Tradeoff between evaluation speed and accuracy | +| context | Message | Retrieved context from your RAG system | +| query | Message | The original user query | +| response | Message | OpenAI's, Claude, etc. model's response based on the context and query | +| run_context_sufficiency | bool | Evaluate whether context supports answering the query | +| run_response_groundedness| bool | Evaluate whether the response is grounded in the context | +| run_response_helpfulness | bool | Evaluate how helpful the response is | +| run_query_ease | bool | Evaluate if the query is vague, complex, or adversarial | + +#### Outputs + +| Name | Type | Description | +|-----------------------|------------|-----------------------------------------------------------------------------| +| trust_score | number | Overall trust score | +| trust_explanation | Message | Explanation for trust score | +| other_scores | dict | Dictionary of optional enabled RAG evaluation metrics | +| evaluation_summary | Message | Markdown summary of query, context, response, and evaluation results | + +--- + +## Example Flows + +The following example flows show how to use the `CleanlabEvaluator` and `CleanlabRemediator` components to evaluate and remediate responses from any LLM, and how to use the `CleanlabRAGEvaluator` component to evaluate RAG pipeline outputs. + +### Evaluate and remediate responses from any LLM + +[Download](./eval_and_remediate_cleanlab.json) the flow to follow along! + +This flow evaluates and remediates the trustworthiness of a response from any LLM using the `CleanlabEvaluator` and `CleanlabRemediator` components. + +![Evaluate response trustworthiness](./eval_response.png) + +Simply connect the `Message` output from any LLM component (like OpenAI, Anthropic, or Google) to the `response` input of the `CleanlabEvaluator` component, along with connecting your prompt to its `prompt` input. + +That's it! The `CleanlabEvaluator` component will return a trust score and explanation which you can use however you'd like. + +The `CleanlabRemediator` component uses this trust score and user configurable settings to determine whether to output the original response, warn about it, or replace it with a fallback answer. + +The example below shows a response that was determined to be untrustworthy (score of .09) and flagged with a warning by the `CleanlabRemediator` component. + +![CleanlabRemediator Example](./cleanlab_remediator_example.png) + +If you don't want to show untrustworthy responses, you can also configure the `CleanlabRemediator` to replace the response with a fallback message. + +![CleanlabRemediator Example](./cleanlab_remediator_example_fallback.png) + +### Evaluate RAG pipeline + +The below flow is the `Vector Store RAG` example template, with the `CleanlabRAGEvaluator` component added to evaluate the context, query, and response. You can use the `CleanlabRAGEvaluator` with any flow that has a context, query, and response. Simply connect the `context`, `query`, and `response` outputs from any RAG pipeline to the `CleanlabRAGEvaluator` component. + +![Evaluate RAG pipeline](./eval_rag.png) + +Here is an example of the `Evaluation Summary` output from the `CleanlabRAGEvaluator` component. + +![Evaluate RAG pipeline](./eval_summary_rag.png) + +Notice how the `Evaluation Summary` includes the query, context, response, and all the evaluation results! In this example, the `Context Sufficiency` and `Response Groundedness` scores are low (0.002) because the context doesn't contain information about the query and the response is not grounded in the context. \ No newline at end of file diff --git a/docs/sidebars.js b/docs/sidebars.js index 757f0910a885..6268e277bbc2 100644 --- a/docs/sidebars.js +++ b/docs/sidebars.js @@ -237,6 +237,11 @@ module.exports = { id: "Integrations/Composio/integrations-composio", label: "Composio", }, + { + type: "doc", + id: "Integrations/Cleanlab/integrations-cleanlab", + label: "Cleanlab", + }, { type: 'category', label: 'Google', diff --git a/pyproject.toml b/pyproject.toml index 056065446d90..4ba71734bfb6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -124,6 +124,7 @@ dependencies = [ "langchain-ibm>=0.3.8", "opik>=1.6.3", "openai>=1.68.2", + "cleanlab-tlm>=1.1.2", 'gassist>=0.0.1; sys_platform == "win32"', "twelvelabs>=0.4.7", ] diff --git a/src/backend/base/langflow/components/cleanlab/__init__.py b/src/backend/base/langflow/components/cleanlab/__init__.py new file mode 100644 index 000000000000..a0549ae05cc7 --- /dev/null +++ b/src/backend/base/langflow/components/cleanlab/__init__.py @@ -0,0 +1,5 @@ +from .cleanlab_evaluator import CleanlabEvaluator +from .cleanlab_rag_evaluator import CleanlabRAGEvaluator +from .cleanlab_remediator import CleanlabRemediator + +__all__ = ["CleanlabEvaluator", "CleanlabRAGEvaluator", "CleanlabRemediator"] diff --git a/src/backend/base/langflow/components/cleanlab/cleanlab_evaluator.py b/src/backend/base/langflow/components/cleanlab/cleanlab_evaluator.py new file mode 100644 index 000000000000..fbd2495c4063 --- /dev/null +++ b/src/backend/base/langflow/components/cleanlab/cleanlab_evaluator.py @@ -0,0 +1,145 @@ +from cleanlab_tlm import TLM + +from langflow.custom import Component +from langflow.io import ( + DropdownInput, + MessageTextInput, + Output, + SecretStrInput, +) +from langflow.schema.message import Message + + +class CleanlabEvaluator(Component): + """A component that evaluates the trustworthiness of LLM responses using Cleanlab. + + This component takes a prompt and response pair, along with optional system instructions, + and uses Cleanlab's evaluation algorithms to generate a trust score and explanation. + + Inputs: + - system_prompt (MessageTextInput): Optional system-level instructions prepended to the user prompt. + - prompt (MessageTextInput): The user's prompt or query sent to the LLM. + - response (MessageTextInput): The response generated by the LLM to be evaluated. This should come from the + LLM component, i.e. OpenAI, Gemini, etc. + - api_key (SecretStrInput): Your Cleanlab API key. + - model (DropdownInput): The model used by Cleanlab to evaluate the response (can differ from the + generation model). + - quality_preset (DropdownInput): Tradeoff setting for accuracy vs. speed and cost. Higher presets are + slower but more accurate. + + Outputs: + - response_passthrough (Message): The original response, passed through for downstream use. + - score (number): A float between 0 and 1 indicating Cleanlab's trustworthiness score for the response. + - explanation (Message): A textual explanation of why the response received its score. + + This component works well in conjunction with the CleanlabRemediator to create a complete trust evaluation + and remediation pipeline. + + More details on the evaluation metrics can be found here: https://help.cleanlab.ai/tlm/tutorials/tlm/ + """ + + display_name = "Cleanlab Evaluator" + description = "Evaluates any LLM response using Cleanlab and outputs trust score and explanation." + icon = "Cleanlab" + name = "CleanlabEvaluator" + + inputs = [ + MessageTextInput( + name="system_prompt", + display_name="System Message", + info="System-level instructions prepended to the user query.", + value="", + ), + MessageTextInput( + name="prompt", + display_name="Prompt", + info="The user's query to the model.", + required=True, + ), + MessageTextInput( + name="response", + display_name="Response", + info="The response to the user's query.", + required=True, + ), + SecretStrInput( + name="api_key", + display_name="Cleanlab API Key", + info="Your Cleanlab API key.", + required=True, + ), + DropdownInput( + name="model", + display_name="Cleanlab Evaluation Model", + options=[ + "gpt-4.1", + "gpt-4.1-mini", + "gpt-4.1-nano", + "o4-mini", + "o3", + "gpt-4.5-preview", + "gpt-4o-mini", + "gpt-4o", + "o3-mini", + "o1", + "o1-mini", + "gpt-4", + "gpt-3.5-turbo-16k", + "claude-3.7-sonnet", + "claude-3.5-sonnet-v2", + "claude-3.5-sonnet", + "claude-3.5-haiku", + "claude-3-haiku", + "nova-micro", + "nova-lite", + "nova-pro", + ], + info="The model Cleanlab uses to evaluate the response. This does NOT need to be the same model that " + "generated the response.", + value="gpt-4o-mini", + required=True, + advanced=True, + ), + DropdownInput( + name="quality_preset", + display_name="Quality Preset", + options=["base", "low", "medium", "high", "best"], + value="medium", + info="This determines the accuracy, latency, and cost of the evaluation. Higher quality is generally " + "slower but more accurate.", + required=True, + advanced=True, + ), + ] + + outputs = [ + Output(display_name="Response", name="response_passthrough", method="pass_response", types=["Message"]), + Output(display_name="Trust Score", name="score", method="get_score", types=["number"]), + Output(display_name="Explanation", name="explanation", method="get_explanation", types=["Message"]), + ] + + def _evaluate_once(self): + if not hasattr(self, "_cached_result"): + full_prompt = f"{self.system_prompt}\n\n{self.prompt}" if self.system_prompt else self.prompt + tlm = TLM( + api_key=self.api_key, + options={"log": ["explanation"], "model": self.model}, + quality_preset=self.quality_preset, + ) + self._cached_result = tlm.get_trustworthiness_score(full_prompt, self.response) + return self._cached_result + + def get_score(self) -> float: + result = self._evaluate_once() + score = result.get("trustworthiness_score", 0.0) + self.status = f"Trust score: {score:.2f}" + return score + + def get_explanation(self) -> Message: + result = self._evaluate_once() + explanation = result.get("log", {}).get("explanation", "No explanation returned.") + return Message(text=explanation) + + def pass_response(self) -> Message: + self.status = "Passing through response." + return Message(text=self.response) diff --git a/src/backend/base/langflow/components/cleanlab/cleanlab_rag_evaluator.py b/src/backend/base/langflow/components/cleanlab/cleanlab_rag_evaluator.py new file mode 100644 index 000000000000..e90a8dd2f2e7 --- /dev/null +++ b/src/backend/base/langflow/components/cleanlab/cleanlab_rag_evaluator.py @@ -0,0 +1,254 @@ +from cleanlab_tlm import TrustworthyRAG, get_default_evals + +from langflow.custom import Component +from langflow.io import ( + BoolInput, + DropdownInput, + MessageTextInput, + Output, + SecretStrInput, +) +from langflow.schema.message import Message + + +class CleanlabRAGEvaluator(Component): + """A component that evaluates the quality of RAG (Retrieval-Augmented Generation) outputs using Cleanlab. + + This component takes a query, retrieved context, and generated response from a RAG pipeline, + and uses Cleanlab's evaluation algorithms to assess various aspects of the RAG system's performance. + + The component can evaluate: + - Overall trustworthiness of the LLM generated response + - Context sufficiency (whether the retrieved context contains information needed to answer the query) + - Response groundedness (whether the response is supported directly by the context) + - Response helpfulness (whether the response effectively addresses the user's query) + - Query ease (whether the user query seems easy for an AI system to properly handle, useful to diagnose + queries that are: complex, vague, tricky, or disgruntled-sounding) + + Outputs: + - Trust Score: A score between 0-1 corresponding to the trustworthiness of the response. A higher score + indicates a higher confidence that the response is correct/good. + - Explanation: An LLM generated explanation of the trustworthiness assessment + - Other Evals: Additional evaluation metrics for selected evaluation types in the "Controls" tab + - Evaluation Summary: A comprehensive summary of context, query, response, and selected evaluation results + + This component works well in conjunction with the CleanlabRemediator to create a complete trust evaluation + and remediation pipeline. + + More details on the evaluation metrics can be found here: https://help.cleanlab.ai/tlm/use-cases/tlm_rag/ + """ + + display_name = "Cleanlab RAG Evaluator" + description = "Evaluates context, query, and response from a RAG pipeline using Cleanlab and outputs trust metrics." + icon = "Cleanlab" + name = "CleanlabRAGEvaluator" + + inputs = [ + SecretStrInput( + name="api_key", + display_name="Cleanlab API Key", + info="Your Cleanlab API key.", + required=True, + ), + DropdownInput( + name="model", + display_name="Cleanlab Evaluation Model", + options=[ + "gpt-4.1", + "gpt-4.1-mini", + "gpt-4.1-nano", + "o4-mini", + "o3", + "gpt-4.5-preview", + "gpt-4o-mini", + "gpt-4o", + "o3-mini", + "o1", + "o1-mini", + "gpt-4", + "gpt-3.5-turbo-16k", + "claude-3.7-sonnet", + "claude-3.5-sonnet-v2", + "claude-3.5-sonnet", + "claude-3.5-haiku", + "claude-3-haiku", + "nova-micro", + "nova-lite", + "nova-pro", + ], + info="The model Cleanlab uses to evaluate the context, query, and response. This does NOT need to be " + "the same model that generated the response.", + value="gpt-4o-mini", + required=True, + advanced=True, + ), + DropdownInput( + name="quality_preset", + display_name="Quality Preset", + options=["base", "low", "medium"], + value="medium", + info="This determines the accuracy, latency, and cost of the evaluation. Higher quality is generally " + "slower but more accurate.", + required=True, + advanced=True, + ), + MessageTextInput( + name="context", + display_name="Context", + info="The context retrieved for the given query.", + required=True, + ), + MessageTextInput( + name="query", + display_name="Query", + info="The user's query.", + required=True, + ), + MessageTextInput( + name="response", + display_name="Response", + info="The response generated by the LLM.", + required=True, + ), + BoolInput( + name="run_context_sufficiency", + display_name="Run Context Sufficiency", + value=False, + advanced=True, + ), + BoolInput( + name="run_response_groundedness", + display_name="Run Response Groundedness", + value=False, + advanced=True, + ), + BoolInput( + name="run_response_helpfulness", + display_name="Run Response Helpfulness", + value=False, + advanced=True, + ), + BoolInput( + name="run_query_ease", + display_name="Run Query Ease", + value=False, + advanced=True, + ), + ] + + outputs = [ + Output(display_name="Response", name="response_passthrough", method="pass_response", types=["Message"]), + Output(display_name="Trust Score", name="trust_score", method="get_trust_score", types=["number"]), + Output(display_name="Explanation", name="trust_explanation", method="get_trust_explanation", types=["Message"]), + Output(display_name="Other Evals", name="other_scores", method="get_other_scores", types=["Data"]), + Output( + display_name="Evaluation Summary", + name="evaluation_summary", + method="get_evaluation_summary", + types=["Message"], + ), + ] + + def _evaluate_once(self): + if not hasattr(self, "_cached_result"): + try: + self.status = "Configuring selected evals..." + default_evals = get_default_evals() + enabled_names = [] + if self.run_context_sufficiency: + enabled_names.append("context_sufficiency") + if self.run_response_groundedness: + enabled_names.append("response_groundedness") + if self.run_response_helpfulness: + enabled_names.append("response_helpfulness") + if self.run_query_ease: + enabled_names.append("query_ease") + + selected_evals = [e for e in default_evals if e.name in enabled_names] + + validator = TrustworthyRAG( + api_key=self.api_key, + quality_preset=self.quality_preset, + options={"log": ["explanation"], "model": self.model}, + evals=selected_evals, + ) + + self.status = f"Running evals: {[e.name for e in selected_evals]}" + self._cached_result = validator.score( + query=self.query, + context=self.context, + response=self.response, + ) + self.status = "Evaluation complete." + + except Exception as e: # noqa: BLE001 + self.status = f"Evaluation failed: {e!s}" + self._cached_result = {} + return self._cached_result + + def pass_response(self) -> Message: + self.status = "Passing through response." + return Message(text=self.response) + + def get_trust_score(self) -> float: + score = self._evaluate_once().get("trustworthiness", {}).get("score", 0.0) + self.status = f"Trust Score: {score:.3f}" + return score + + def get_trust_explanation(self) -> Message: + explanation = self._evaluate_once().get("trustworthiness", {}).get("log", {}).get("explanation", "") + self.status = "Trust explanation extracted." + return Message(text=explanation) + + def get_other_scores(self) -> dict: + result = self._evaluate_once() + + selected = { + "context_sufficiency": self.run_context_sufficiency, + "response_groundedness": self.run_response_groundedness, + "response_helpfulness": self.run_response_helpfulness, + "query_ease": self.run_query_ease, + } + + filtered_scores = {key: result[key]["score"] for key, include in selected.items() if include and key in result} + + self.status = f"{len(filtered_scores)} other evals returned." + return filtered_scores + + def get_evaluation_summary(self) -> Message: + result = self._evaluate_once() + + query_text = self.query.strip() + context_text = self.context.strip() + response_text = self.response.strip() + + trust = result.get("trustworthiness", {}).get("score", 0.0) + trust_exp = result.get("trustworthiness", {}).get("log", {}).get("explanation", "") + + selected = { + "context_sufficiency": self.run_context_sufficiency, + "response_groundedness": self.run_response_groundedness, + "response_helpfulness": self.run_response_helpfulness, + "query_ease": self.run_query_ease, + } + + other_scores = {key: result[key]["score"] for key, include in selected.items() if include and key in result} + + metrics = f"Trustworthiness: {trust:.3f}" + if trust_exp: + metrics += f"\nExplanation: {trust_exp}" + if other_scores: + metrics += "\n" + "\n".join(f"{k.replace('_', ' ').title()}: {v:.3f}" for k, v in other_scores.items()) + + summary = ( + f"Query:\n{query_text}\n" + "-----\n" + f"Context:\n{context_text}\n" + "-----\n" + f"Response:\n{response_text}\n" + "------------------------------\n" + f"{metrics}" + ) + + self.status = "Evaluation summary built." + return Message(text=summary) diff --git a/src/backend/base/langflow/components/cleanlab/cleanlab_remediator.py b/src/backend/base/langflow/components/cleanlab/cleanlab_remediator.py new file mode 100644 index 000000000000..0cfb5cc4d2c5 --- /dev/null +++ b/src/backend/base/langflow/components/cleanlab/cleanlab_remediator.py @@ -0,0 +1,131 @@ +from langflow.custom import Component +from langflow.field_typing.range_spec import RangeSpec +from langflow.io import BoolInput, FloatInput, HandleInput, MessageTextInput, Output, PromptInput +from langflow.schema.message import Message + + +class CleanlabRemediator(Component): + """Remediates potentially untrustworthy LLM responses based on trust scores computed by the Cleanlab Evaluator. + + This component takes a response and its associated trust score, + and applies remediation strategies based on configurable thresholds and settings. + + Inputs: + - response (MessageTextInput): The original LLM-generated response to be evaluated and possibly remediated. + The CleanlabEvaluator passes this response through. + - score (HandleInput): The trust score output from CleanlabEvaluator (expected to be a float between 0 and 1). + - explanation (MessageTextInput): Optional textual explanation for the trust score, to be included in the + output. + - threshold (Input[float]): Minimum trust score required to accept the response. If the score is lower, the + response is remediated. + - show_untrustworthy_response (BoolInput): If true, returns the original response with a warning; if false, + returns fallback text. + - untrustworthy_warning_text (PromptInput): Text warning to append to responses deemed untrustworthy (when + showing them). + - fallback_text (PromptInput): Replacement message returned if the response is untrustworthy and should be + hidden. + + Outputs: + - remediated_response (Message): Either: + • the original response, + • the original response with appended warning, or + • the fallback response, + depending on the trust score and configuration. + + This component is typically used downstream of CleanlabEvaluator or CleanlabRagValidator + to take appropriate action on low-trust responses and inform users accordingly. + """ + + display_name = "Cleanlab Remediator" + description = ( + "Remediates an untrustworthy response based on trust score from the Cleanlab Evaluator, " + "score threshold, and message handling settings." + ) + icon = "Cleanlab" + name = "CleanlabRemediator" + + inputs = [ + MessageTextInput( + name="response", + display_name="Response", + info="The response to the user's query.", + required=True, + ), + HandleInput( + name="score", + display_name="Trust Score", + info="The trustworthiness score output from the Cleanlab Evaluator.", + input_types=["number"], + required=True, + ), + MessageTextInput( + name="explanation", + display_name="Explanation", + info="The explanation from the Cleanlab Evaluator.", + required=False, + ), + FloatInput( + name="threshold", + display_name="Threshold", + field_type="float", + value=0.7, + range_spec=RangeSpec(min=0.0, max=1.0, step=0.05), + info="Minimum score required to show the response unmodified. Reponses with scores above this threshold " + "are considered trustworthy. Reponses with scores below this threshold are considered untrustworthy and " + "will be remediated based on the settings below.", + required=True, + show=True, + ), + BoolInput( + name="show_untrustworthy_response", + display_name="Show Untrustworthy Response", + info="If enabled, and the trust score is below the threshold, the original response is shown with the " + "added warning. If disabled, and the trust score is below the threshold, the fallback answer is returned.", + value=True, + ), + PromptInput( + name="untrustworthy_warning_text", + display_name="Warning for Untrustworthy Response", + info="Warning to append to the response if Show Untrustworthy Response is enabled and trust score is " + "below the threshold.", + value="⚠️ WARNING: The following response is potentially untrustworthy.", + ), + PromptInput( + name="fallback_text", + display_name="Fallback Answer", + info="Response returned if the trust score is below the threshold and 'Show Untrustworthy Response' is " + "disabled.", + value="Based on the available information, I cannot provide a complete answer to this question.", + ), + ] + + outputs = [ + Output( + display_name="Remediated Message", + name="remediated_response", + method="remediate_response", + types=["Message"], + ), + ] + + def remediate_response(self) -> Message: + if self.score >= self.threshold: + self.status = f"Score {self.score:.2f} ≥ threshold {self.threshold:.2f} → accepted" + return Message( + text=f"{self.response}\n\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n\n**Trust Score:** {self.score:.2f}" + ) + + self.status = f"Score {self.score:.2f} < threshold {self.threshold:.2f} → flagged" + + if self.show_untrustworthy_response: + parts = [ + self.response, + "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━", + f"**{self.untrustworthy_warning_text.strip()}**", + f"**Trust Score:** {self.score:.2f}", + ] + if self.explanation: + parts.append(f"**Explanation:** {self.explanation}") + return Message(text="\n\n".join(parts)) + + return Message(text=self.fallback_text) diff --git "a/src/backend/base/langflow/initial_setup/starter_projects/Pok\303\251dex Agent.json" "b/src/backend/base/langflow/initial_setup/starter_projects/Pok\303\251dex Agent.json" index 5f06ff8e7912..69265210df53 100644 --- "a/src/backend/base/langflow/initial_setup/starter_projects/Pok\303\251dex Agent.json" +++ "b/src/backend/base/langflow/initial_setup/starter_projects/Pok\303\251dex Agent.json" @@ -922,7 +922,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import json\nimport re\nimport tempfile\nfrom datetime import datetime, timezone\nfrom pathlib import Path\nfrom typing import Any\nfrom urllib.parse import parse_qsl, urlencode, urlparse, urlunparse\n\nimport aiofiles\nimport aiofiles.os as aiofiles_os\nimport httpx\nimport validators\n\nfrom langflow.base.curl.parse import parse_context\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import TabInput\nfrom langflow.io import (\n BoolInput,\n DataInput,\n DropdownInput,\n IntInput,\n MessageTextInput,\n MultilineInput,\n Output,\n TableInput,\n)\nfrom langflow.schema import Data\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.services.deps import get_settings_service\nfrom langflow.utils.component_utils import set_current_fields, set_field_advanced, set_field_display\n\n# Define fields for each mode\nMODE_FIELDS = {\n \"URL\": [\n \"url_input\",\n \"method\",\n ],\n \"cURL\": [\"curl_input\"],\n}\n\n# Fields that should always be visible\nDEFAULT_FIELDS = [\"mode\"]\n\n\nclass APIRequestComponent(Component):\n display_name = \"API Request\"\n description = \"Make HTTP requests using URL or cURL commands.\"\n icon = \"Globe\"\n name = \"APIRequest\"\n\n inputs = [\n MessageTextInput(\n name=\"url_input\",\n display_name=\"URL\",\n info=\"Enter the URL for the request.\",\n advanced=False,\n tool_mode=True,\n ),\n MultilineInput(\n name=\"curl_input\",\n display_name=\"cURL\",\n info=(\n \"Paste a curl command to populate the fields. \"\n \"This will fill in the dictionary fields for headers and body.\"\n ),\n real_time_refresh=True,\n tool_mode=True,\n advanced=True,\n show=False,\n ),\n DropdownInput(\n name=\"method\",\n display_name=\"Method\",\n options=[\"GET\", \"POST\", \"PATCH\", \"PUT\", \"DELETE\"],\n value=\"GET\",\n info=\"The HTTP method to use.\",\n real_time_refresh=True,\n ),\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"URL\", \"cURL\"],\n value=\"URL\",\n info=\"Enable cURL mode to populate fields from a cURL command.\",\n real_time_refresh=True,\n ),\n DataInput(\n name=\"query_params\",\n display_name=\"Query Parameters\",\n info=\"The query parameters to append to the URL.\",\n advanced=True,\n ),\n TableInput(\n name=\"body\",\n display_name=\"Body\",\n info=\"The body to send with the request as a dictionary (for POST, PATCH, PUT).\",\n table_schema=[\n {\n \"name\": \"key\",\n \"display_name\": \"Key\",\n \"type\": \"str\",\n \"description\": \"Parameter name\",\n },\n {\n \"name\": \"value\",\n \"display_name\": \"Value\",\n \"description\": \"Parameter value\",\n },\n ],\n value=[],\n input_types=[\"Data\"],\n advanced=True,\n real_time_refresh=True,\n ),\n TableInput(\n name=\"headers\",\n display_name=\"Headers\",\n info=\"The headers to send with the request\",\n table_schema=[\n {\n \"name\": \"key\",\n \"display_name\": \"Header\",\n \"type\": \"str\",\n \"description\": \"Header name\",\n },\n {\n \"name\": \"value\",\n \"display_name\": \"Value\",\n \"type\": \"str\",\n \"description\": \"Header value\",\n },\n ],\n value=[{\"key\": \"User-Agent\", \"value\": get_settings_service().settings.user_agent}],\n advanced=True,\n input_types=[\"Data\"],\n real_time_refresh=True,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n value=30,\n info=\"The timeout to use for the request.\",\n advanced=True,\n ),\n BoolInput(\n name=\"follow_redirects\",\n display_name=\"Follow Redirects\",\n value=True,\n info=\"Whether to follow http redirects.\",\n advanced=True,\n ),\n BoolInput(\n name=\"save_to_file\",\n display_name=\"Save to File\",\n value=False,\n info=\"Save the API response to a temporary file\",\n advanced=True,\n ),\n BoolInput(\n name=\"include_httpx_metadata\",\n display_name=\"Include HTTPx Metadata\",\n value=False,\n info=(\n \"Include properties such as headers, status_code, response_headers, \"\n \"and redirection_history in the output.\"\n ),\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"API Response\", name=\"data\", method=\"make_api_request\"),\n ]\n\n def _parse_json_value(self, value: Any) -> Any:\n \"\"\"Parse a value that might be a JSON string.\"\"\"\n if not isinstance(value, str):\n return value\n\n try:\n parsed = json.loads(value)\n except json.JSONDecodeError:\n return value\n else:\n return parsed\n\n def _process_body(self, body: Any) -> dict:\n \"\"\"Process the body input into a valid dictionary.\"\"\"\n if body is None:\n return {}\n if isinstance(body, dict):\n return self._process_dict_body(body)\n if isinstance(body, str):\n return self._process_string_body(body)\n if isinstance(body, list):\n return self._process_list_body(body)\n return {}\n\n def _process_dict_body(self, body: dict) -> dict:\n \"\"\"Process dictionary body by parsing JSON values.\"\"\"\n return {k: self._parse_json_value(v) for k, v in body.items()}\n\n def _process_string_body(self, body: str) -> dict:\n \"\"\"Process string body by attempting JSON parse.\"\"\"\n try:\n return self._process_body(json.loads(body))\n except json.JSONDecodeError:\n return {\"data\": body}\n\n def _process_list_body(self, body: list) -> dict:\n \"\"\"Process list body by converting to key-value dictionary.\"\"\"\n processed_dict = {}\n try:\n for item in body:\n if not self._is_valid_key_value_item(item):\n continue\n key = item[\"key\"]\n value = self._parse_json_value(item[\"value\"])\n processed_dict[key] = value\n except (KeyError, TypeError, ValueError) as e:\n self.log(f\"Failed to process body list: {e}\")\n return {}\n return processed_dict\n\n def _is_valid_key_value_item(self, item: Any) -> bool:\n \"\"\"Check if an item is a valid key-value dictionary.\"\"\"\n return isinstance(item, dict) and \"key\" in item and \"value\" in item\n\n\n def parse_curl(self, curl: str, build_config: dotdict) -> dotdict:\n \"\"\"Parse a cURL command and update build configuration.\"\"\"\n try:\n parsed = parse_context(curl)\n\n # Update basic configuration\n url = parsed.url\n # Normalize URL before setting it\n url = self._normalize_url(url)\n\n build_config[\"url_input\"][\"value\"] = url\n build_config[\"method\"][\"value\"] = parsed.method.upper()\n\n # Process headers\n headers_list = [{\"key\": k, \"value\": v} for k, v in parsed.headers.items()]\n build_config[\"headers\"][\"value\"] = headers_list\n\n # Process body data\n if not parsed.data:\n build_config[\"body\"][\"value\"] = []\n elif parsed.data:\n try:\n json_data = json.loads(parsed.data)\n if isinstance(json_data, dict):\n body_list = [\n {\"key\": k, \"value\": json.dumps(v) if isinstance(v, dict | list) else str(v)}\n for k, v in json_data.items()\n ]\n build_config[\"body\"][\"value\"] = body_list\n else:\n build_config[\"body\"][\"value\"] = [{\"key\": \"data\", \"value\": json.dumps(json_data)}]\n except json.JSONDecodeError:\n build_config[\"body\"][\"value\"] = [{\"key\": \"data\", \"value\": parsed.data}]\n\n except Exception as exc:\n msg = f\"Error parsing curl: {exc}\"\n self.log(msg)\n raise ValueError(msg) from exc\n\n return build_config\n\n def _normalize_url(self, url: str) -> str:\n \"\"\"Normalize URL by adding https:// if no protocol is specified.\"\"\"\n if not url or not isinstance(url, str):\n msg = \"URL cannot be empty\"\n raise ValueError(msg)\n\n url = url.strip()\n if url.startswith((\"http://\", \"https://\")):\n return url\n return f\"https://{url}\"\n\n async def make_request(\n self,\n client: httpx.AsyncClient,\n method: str,\n url: str,\n headers: dict | None = None,\n body: Any = None,\n timeout: int = 5,\n *,\n follow_redirects: bool = True,\n save_to_file: bool = False,\n include_httpx_metadata: bool = False,\n ) -> Data:\n method = method.upper()\n if method not in {\"GET\", \"POST\", \"PATCH\", \"PUT\", \"DELETE\"}:\n msg = f\"Unsupported method: {method}\"\n raise ValueError(msg)\n\n processed_body = self._process_body(body)\n redirection_history = []\n\n try:\n # Prepare request parameters\n request_params = {\n \"method\": method,\n \"url\": url,\n \"headers\": headers,\n \"json\": processed_body,\n \"timeout\": timeout,\n \"follow_redirects\": follow_redirects,\n }\n response = await client.request(**request_params)\n\n redirection_history = [\n {\n \"url\": redirect.headers.get(\"Location\", str(redirect.url)),\n \"status_code\": redirect.status_code,\n }\n for redirect in response.history\n ]\n\n is_binary, file_path = await self._response_info(response, with_file_path=save_to_file)\n response_headers = self._headers_to_dict(response.headers)\n\n # Base metadata\n metadata = {\n \"source\": url,\n \"status_code\": response.status_code,\n \"response_headers\": response_headers,\n }\n\n if redirection_history:\n metadata[\"redirection_history\"] = redirection_history\n\n if save_to_file:\n mode = \"wb\" if is_binary else \"w\"\n encoding = response.encoding if mode == \"w\" else None\n if file_path:\n await aiofiles_os.makedirs(file_path.parent, exist_ok=True)\n if is_binary:\n async with aiofiles.open(file_path, \"wb\") as f:\n await f.write(response.content)\n await f.flush()\n else:\n async with aiofiles.open(file_path, \"w\", encoding=encoding) as f:\n await f.write(response.text)\n await f.flush()\n metadata[\"file_path\"] = str(file_path)\n\n if include_httpx_metadata:\n metadata.update({\"headers\": headers})\n return Data(data=metadata)\n\n # Handle response content\n if is_binary:\n result = response.content\n else:\n try:\n result = response.json()\n except json.JSONDecodeError:\n self.log(\"Failed to decode JSON response\")\n result = response.text.encode(\"utf-8\")\n\n metadata[\"result\"] = result\n\n if include_httpx_metadata:\n metadata.update({\"headers\": headers})\n\n return Data(data=metadata)\n except (httpx.HTTPError, httpx.RequestError, httpx.TimeoutException) as exc:\n self.log(f\"Error making request to {url}\")\n return Data(\n data={\n \"source\": url,\n \"headers\": headers,\n \"status_code\": 500,\n \"error\": str(exc),\n **({\"redirection_history\": redirection_history} if redirection_history else {}),\n },\n )\n\n def add_query_params(self, url: str, params: dict) -> str:\n \"\"\"Add query parameters to URL efficiently.\"\"\"\n if not params:\n return url\n url_parts = list(urlparse(url))\n query = dict(parse_qsl(url_parts[4]))\n query.update(params)\n url_parts[4] = urlencode(query)\n return urlunparse(url_parts)\n\n def _headers_to_dict(self, headers: httpx.Headers) -> dict[str, str]:\n \"\"\"Convert HTTP headers to a dictionary with lowercased keys.\"\"\"\n return {k.lower(): v for k, v in headers.items()}\n\n def _process_headers(self, headers: Any) -> dict:\n \"\"\"Process the headers input into a valid dictionary.\"\"\"\n if headers is None:\n return {}\n if isinstance(headers, dict):\n return headers\n if isinstance(headers, list):\n return {item[\"key\"]: item[\"value\"] for item in headers if self._is_valid_key_value_item(item)}\n return {}\n\n async def make_api_request(self) -> Data:\n \"\"\"Make HTTP request with optimized parameter handling.\"\"\"\n method = self.method\n url = self.url_input.strip() if isinstance(self.url_input, str) else \"\"\n headers = self.headers or {}\n body = self.body or {}\n timeout = self.timeout\n follow_redirects = self.follow_redirects\n save_to_file = self.save_to_file\n include_httpx_metadata = self.include_httpx_metadata\n\n # if self.mode == \"cURL\" and self.curl_input:\n # self._build_config = self.parse_curl(self.curl_input, dotdict())\n # # After parsing curl, get the normalized URL\n # url = self._build_config[\"url_input\"][\"value\"]\n\n # Normalize URL before validation\n url = self._normalize_url(url)\n\n # Validate URL\n if not validators.url(url):\n msg = f\"Invalid URL provided: {url}\"\n raise ValueError(msg)\n\n # Process query parameters\n if isinstance(self.query_params, str):\n query_params = dict(parse_qsl(self.query_params))\n else:\n query_params = self.query_params.data if self.query_params else {}\n\n # Process headers and body\n headers = self._process_headers(headers)\n body = self._process_body(body)\n url = self.add_query_params(url, query_params)\n\n async with httpx.AsyncClient() as client:\n result = await self.make_request(\n client,\n method,\n url,\n headers,\n body,\n timeout,\n follow_redirects=follow_redirects,\n save_to_file=save_to_file,\n include_httpx_metadata=include_httpx_metadata,\n )\n self.status = result\n return result\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n \"\"\"Update the build config based on the selected mode.\"\"\"\n if field_name != \"mode\":\n if field_name == \"curl_input\" and self.mode == \"cURL\" and self.curl_input:\n return self.parse_curl(self.curl_input, build_config)\n return build_config\n\n # print(f\"Current mode: {field_value}\")\n if field_value == \"cURL\":\n set_field_display(build_config, \"curl_input\", value=True)\n if build_config[\"curl_input\"][\"value\"]:\n build_config = self.parse_curl(build_config[\"curl_input\"][\"value\"], build_config)\n else:\n set_field_display(build_config, \"curl_input\", value=False)\n\n return set_current_fields(\n build_config=build_config,\n action_fields=MODE_FIELDS,\n selected_action=field_value,\n default_fields=DEFAULT_FIELDS,\n func=set_field_advanced,\n default_value=True,\n )\n\n async def _response_info(\n self, response: httpx.Response, *, with_file_path: bool = False\n ) -> tuple[bool, Path | None]:\n \"\"\"Determine the file path and whether the response content is binary.\n\n Args:\n response (Response): The HTTP response object.\n with_file_path (bool): Whether to save the response content to a file.\n\n Returns:\n Tuple[bool, Path | None]:\n A tuple containing a boolean indicating if the content is binary and the full file path (if applicable).\n \"\"\"\n content_type = response.headers.get(\"Content-Type\", \"\")\n is_binary = \"application/octet-stream\" in content_type or \"application/binary\" in content_type\n\n if not with_file_path:\n return is_binary, None\n\n component_temp_dir = Path(tempfile.gettempdir()) / self.__class__.__name__\n\n # Create directory asynchronously\n await aiofiles_os.makedirs(component_temp_dir, exist_ok=True)\n\n filename = None\n if \"Content-Disposition\" in response.headers:\n content_disposition = response.headers[\"Content-Disposition\"]\n filename_match = re.search(r'filename=\"(.+?)\"', content_disposition)\n if filename_match:\n extracted_filename = filename_match.group(1)\n filename = extracted_filename\n\n # Step 3: Infer file extension or use part of the request URL if no filename\n if not filename:\n # Extract the last segment of the URL path\n url_path = urlparse(str(response.request.url) if response.request else \"\").path\n base_name = Path(url_path).name # Get the last segment of the path\n if not base_name: # If the path ends with a slash or is empty\n base_name = \"response\"\n\n # Infer file extension\n content_type_to_extension = {\n \"text/plain\": \".txt\",\n \"application/json\": \".json\",\n \"image/jpeg\": \".jpg\",\n \"image/png\": \".png\",\n \"application/octet-stream\": \".bin\",\n }\n extension = content_type_to_extension.get(content_type, \".bin\" if is_binary else \".txt\")\n filename = f\"{base_name}{extension}\"\n\n # Step 4: Define the full file path\n file_path = component_temp_dir / filename\n\n # Step 5: Check if file exists asynchronously and handle accordingly\n try:\n # Try to create the file exclusively (x mode) to check existence\n async with aiofiles.open(file_path, \"x\") as _:\n pass # File created successfully, we can use this path\n except FileExistsError:\n # If file exists, append a timestamp to the filename\n timestamp = datetime.now(timezone.utc).strftime(\"%Y%m%d%H%M%S%f\")\n file_path = component_temp_dir / f\"{timestamp}-{filename}\"\n\n return is_binary, file_path\n" + "value": "import json\nimport re\nimport tempfile\nfrom datetime import datetime, timezone\nfrom pathlib import Path\nfrom typing import Any\nfrom urllib.parse import parse_qsl, urlencode, urlparse, urlunparse\n\nimport aiofiles\nimport aiofiles.os as aiofiles_os\nimport httpx\nimport validators\n\nfrom langflow.base.curl.parse import parse_context\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import TabInput\nfrom langflow.io import (\n BoolInput,\n DataInput,\n DropdownInput,\n IntInput,\n MessageTextInput,\n MultilineInput,\n Output,\n TableInput,\n)\nfrom langflow.schema import Data\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.services.deps import get_settings_service\nfrom langflow.utils.component_utils import set_current_fields, set_field_advanced, set_field_display\n\n# Define fields for each mode\nMODE_FIELDS = {\n \"URL\": [\n \"url_input\",\n \"method\",\n ],\n \"cURL\": [\"curl_input\"],\n}\n\n# Fields that should always be visible\nDEFAULT_FIELDS = [\"mode\"]\n\n\nclass APIRequestComponent(Component):\n display_name = \"API Request\"\n description = \"Make HTTP requests using URL or cURL commands.\"\n icon = \"Globe\"\n name = \"APIRequest\"\n\n inputs = [\n MessageTextInput(\n name=\"url_input\",\n display_name=\"URL\",\n info=\"Enter the URL for the request.\",\n advanced=False,\n tool_mode=True,\n ),\n MultilineInput(\n name=\"curl_input\",\n display_name=\"cURL\",\n info=(\n \"Paste a curl command to populate the fields. \"\n \"This will fill in the dictionary fields for headers and body.\"\n ),\n real_time_refresh=True,\n tool_mode=True,\n advanced=True,\n show=False,\n ),\n DropdownInput(\n name=\"method\",\n display_name=\"Method\",\n options=[\"GET\", \"POST\", \"PATCH\", \"PUT\", \"DELETE\"],\n value=\"GET\",\n info=\"The HTTP method to use.\",\n real_time_refresh=True,\n ),\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"URL\", \"cURL\"],\n value=\"URL\",\n info=\"Enable cURL mode to populate fields from a cURL command.\",\n real_time_refresh=True,\n ),\n DataInput(\n name=\"query_params\",\n display_name=\"Query Parameters\",\n info=\"The query parameters to append to the URL.\",\n advanced=True,\n ),\n TableInput(\n name=\"body\",\n display_name=\"Body\",\n info=\"The body to send with the request as a dictionary (for POST, PATCH, PUT).\",\n table_schema=[\n {\n \"name\": \"key\",\n \"display_name\": \"Key\",\n \"type\": \"str\",\n \"description\": \"Parameter name\",\n },\n {\n \"name\": \"value\",\n \"display_name\": \"Value\",\n \"description\": \"Parameter value\",\n },\n ],\n value=[],\n input_types=[\"Data\"],\n advanced=True,\n real_time_refresh=True,\n ),\n TableInput(\n name=\"headers\",\n display_name=\"Headers\",\n info=\"The headers to send with the request\",\n table_schema=[\n {\n \"name\": \"key\",\n \"display_name\": \"Header\",\n \"type\": \"str\",\n \"description\": \"Header name\",\n },\n {\n \"name\": \"value\",\n \"display_name\": \"Value\",\n \"type\": \"str\",\n \"description\": \"Header value\",\n },\n ],\n value=[{\"key\": \"User-Agent\", \"value\": get_settings_service().settings.user_agent}],\n advanced=True,\n input_types=[\"Data\"],\n real_time_refresh=True,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n value=30,\n info=\"The timeout to use for the request.\",\n advanced=True,\n ),\n BoolInput(\n name=\"follow_redirects\",\n display_name=\"Follow Redirects\",\n value=True,\n info=\"Whether to follow http redirects.\",\n advanced=True,\n ),\n BoolInput(\n name=\"save_to_file\",\n display_name=\"Save to File\",\n value=False,\n info=\"Save the API response to a temporary file\",\n advanced=True,\n ),\n BoolInput(\n name=\"include_httpx_metadata\",\n display_name=\"Include HTTPx Metadata\",\n value=False,\n info=(\n \"Include properties such as headers, status_code, response_headers, \"\n \"and redirection_history in the output.\"\n ),\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"API Response\", name=\"data\", method=\"make_api_request\"),\n ]\n\n def _parse_json_value(self, value: Any) -> Any:\n \"\"\"Parse a value that might be a JSON string.\"\"\"\n if not isinstance(value, str):\n return value\n\n try:\n parsed = json.loads(value)\n except json.JSONDecodeError:\n return value\n else:\n return parsed\n\n def _process_body(self, body: Any) -> dict:\n \"\"\"Process the body input into a valid dictionary.\"\"\"\n if body is None:\n return {}\n if isinstance(body, dict):\n return self._process_dict_body(body)\n if isinstance(body, str):\n return self._process_string_body(body)\n if isinstance(body, list):\n return self._process_list_body(body)\n return {}\n\n def _process_dict_body(self, body: dict) -> dict:\n \"\"\"Process dictionary body by parsing JSON values.\"\"\"\n return {k: self._parse_json_value(v) for k, v in body.items()}\n\n def _process_string_body(self, body: str) -> dict:\n \"\"\"Process string body by attempting JSON parse.\"\"\"\n try:\n return self._process_body(json.loads(body))\n except json.JSONDecodeError:\n return {\"data\": body}\n\n def _process_list_body(self, body: list) -> dict:\n \"\"\"Process list body by converting to key-value dictionary.\"\"\"\n processed_dict = {}\n try:\n for item in body:\n if not self._is_valid_key_value_item(item):\n continue\n key = item[\"key\"]\n value = self._parse_json_value(item[\"value\"])\n processed_dict[key] = value\n except (KeyError, TypeError, ValueError) as e:\n self.log(f\"Failed to process body list: {e}\")\n return {}\n return processed_dict\n\n def _is_valid_key_value_item(self, item: Any) -> bool:\n \"\"\"Check if an item is a valid key-value dictionary.\"\"\"\n return isinstance(item, dict) and \"key\" in item and \"value\" in item\n\n def parse_curl(self, curl: str, build_config: dotdict) -> dotdict:\n \"\"\"Parse a cURL command and update build configuration.\"\"\"\n try:\n parsed = parse_context(curl)\n\n # Update basic configuration\n url = parsed.url\n # Normalize URL before setting it\n url = self._normalize_url(url)\n\n build_config[\"url_input\"][\"value\"] = url\n build_config[\"method\"][\"value\"] = parsed.method.upper()\n\n # Process headers\n headers_list = [{\"key\": k, \"value\": v} for k, v in parsed.headers.items()]\n build_config[\"headers\"][\"value\"] = headers_list\n\n # Process body data\n if not parsed.data:\n build_config[\"body\"][\"value\"] = []\n elif parsed.data:\n try:\n json_data = json.loads(parsed.data)\n if isinstance(json_data, dict):\n body_list = [\n {\"key\": k, \"value\": json.dumps(v) if isinstance(v, dict | list) else str(v)}\n for k, v in json_data.items()\n ]\n build_config[\"body\"][\"value\"] = body_list\n else:\n build_config[\"body\"][\"value\"] = [{\"key\": \"data\", \"value\": json.dumps(json_data)}]\n except json.JSONDecodeError:\n build_config[\"body\"][\"value\"] = [{\"key\": \"data\", \"value\": parsed.data}]\n\n except Exception as exc:\n msg = f\"Error parsing curl: {exc}\"\n self.log(msg)\n raise ValueError(msg) from exc\n\n return build_config\n\n def _normalize_url(self, url: str) -> str:\n \"\"\"Normalize URL by adding https:// if no protocol is specified.\"\"\"\n if not url or not isinstance(url, str):\n msg = \"URL cannot be empty\"\n raise ValueError(msg)\n\n url = url.strip()\n if url.startswith((\"http://\", \"https://\")):\n return url\n return f\"https://{url}\"\n\n async def make_request(\n self,\n client: httpx.AsyncClient,\n method: str,\n url: str,\n headers: dict | None = None,\n body: Any = None,\n timeout: int = 5,\n *,\n follow_redirects: bool = True,\n save_to_file: bool = False,\n include_httpx_metadata: bool = False,\n ) -> Data:\n method = method.upper()\n if method not in {\"GET\", \"POST\", \"PATCH\", \"PUT\", \"DELETE\"}:\n msg = f\"Unsupported method: {method}\"\n raise ValueError(msg)\n\n processed_body = self._process_body(body)\n redirection_history = []\n\n try:\n # Prepare request parameters\n request_params = {\n \"method\": method,\n \"url\": url,\n \"headers\": headers,\n \"json\": processed_body,\n \"timeout\": timeout,\n \"follow_redirects\": follow_redirects,\n }\n response = await client.request(**request_params)\n\n redirection_history = [\n {\n \"url\": redirect.headers.get(\"Location\", str(redirect.url)),\n \"status_code\": redirect.status_code,\n }\n for redirect in response.history\n ]\n\n is_binary, file_path = await self._response_info(response, with_file_path=save_to_file)\n response_headers = self._headers_to_dict(response.headers)\n\n # Base metadata\n metadata = {\n \"source\": url,\n \"status_code\": response.status_code,\n \"response_headers\": response_headers,\n }\n\n if redirection_history:\n metadata[\"redirection_history\"] = redirection_history\n\n if save_to_file:\n mode = \"wb\" if is_binary else \"w\"\n encoding = response.encoding if mode == \"w\" else None\n if file_path:\n await aiofiles_os.makedirs(file_path.parent, exist_ok=True)\n if is_binary:\n async with aiofiles.open(file_path, \"wb\") as f:\n await f.write(response.content)\n await f.flush()\n else:\n async with aiofiles.open(file_path, \"w\", encoding=encoding) as f:\n await f.write(response.text)\n await f.flush()\n metadata[\"file_path\"] = str(file_path)\n\n if include_httpx_metadata:\n metadata.update({\"headers\": headers})\n return Data(data=metadata)\n\n # Handle response content\n if is_binary:\n result = response.content\n else:\n try:\n result = response.json()\n except json.JSONDecodeError:\n self.log(\"Failed to decode JSON response\")\n result = response.text.encode(\"utf-8\")\n\n metadata[\"result\"] = result\n\n if include_httpx_metadata:\n metadata.update({\"headers\": headers})\n\n return Data(data=metadata)\n except (httpx.HTTPError, httpx.RequestError, httpx.TimeoutException) as exc:\n self.log(f\"Error making request to {url}\")\n return Data(\n data={\n \"source\": url,\n \"headers\": headers,\n \"status_code\": 500,\n \"error\": str(exc),\n **({\"redirection_history\": redirection_history} if redirection_history else {}),\n },\n )\n\n def add_query_params(self, url: str, params: dict) -> str:\n \"\"\"Add query parameters to URL efficiently.\"\"\"\n if not params:\n return url\n url_parts = list(urlparse(url))\n query = dict(parse_qsl(url_parts[4]))\n query.update(params)\n url_parts[4] = urlencode(query)\n return urlunparse(url_parts)\n\n def _headers_to_dict(self, headers: httpx.Headers) -> dict[str, str]:\n \"\"\"Convert HTTP headers to a dictionary with lowercased keys.\"\"\"\n return {k.lower(): v for k, v in headers.items()}\n\n def _process_headers(self, headers: Any) -> dict:\n \"\"\"Process the headers input into a valid dictionary.\"\"\"\n if headers is None:\n return {}\n if isinstance(headers, dict):\n return headers\n if isinstance(headers, list):\n return {item[\"key\"]: item[\"value\"] for item in headers if self._is_valid_key_value_item(item)}\n return {}\n\n async def make_api_request(self) -> Data:\n \"\"\"Make HTTP request with optimized parameter handling.\"\"\"\n method = self.method\n url = self.url_input.strip() if isinstance(self.url_input, str) else \"\"\n headers = self.headers or {}\n body = self.body or {}\n timeout = self.timeout\n follow_redirects = self.follow_redirects\n save_to_file = self.save_to_file\n include_httpx_metadata = self.include_httpx_metadata\n\n # if self.mode == \"cURL\" and self.curl_input:\n # self._build_config = self.parse_curl(self.curl_input, dotdict())\n # # After parsing curl, get the normalized URL\n # url = self._build_config[\"url_input\"][\"value\"]\n\n # Normalize URL before validation\n url = self._normalize_url(url)\n\n # Validate URL\n if not validators.url(url):\n msg = f\"Invalid URL provided: {url}\"\n raise ValueError(msg)\n\n # Process query parameters\n if isinstance(self.query_params, str):\n query_params = dict(parse_qsl(self.query_params))\n else:\n query_params = self.query_params.data if self.query_params else {}\n\n # Process headers and body\n headers = self._process_headers(headers)\n body = self._process_body(body)\n url = self.add_query_params(url, query_params)\n\n async with httpx.AsyncClient() as client:\n result = await self.make_request(\n client,\n method,\n url,\n headers,\n body,\n timeout,\n follow_redirects=follow_redirects,\n save_to_file=save_to_file,\n include_httpx_metadata=include_httpx_metadata,\n )\n self.status = result\n return result\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n \"\"\"Update the build config based on the selected mode.\"\"\"\n if field_name != \"mode\":\n if field_name == \"curl_input\" and self.mode == \"cURL\" and self.curl_input:\n return self.parse_curl(self.curl_input, build_config)\n return build_config\n\n # print(f\"Current mode: {field_value}\")\n if field_value == \"cURL\":\n set_field_display(build_config, \"curl_input\", value=True)\n if build_config[\"curl_input\"][\"value\"]:\n build_config = self.parse_curl(build_config[\"curl_input\"][\"value\"], build_config)\n else:\n set_field_display(build_config, \"curl_input\", value=False)\n\n return set_current_fields(\n build_config=build_config,\n action_fields=MODE_FIELDS,\n selected_action=field_value,\n default_fields=DEFAULT_FIELDS,\n func=set_field_advanced,\n default_value=True,\n )\n\n async def _response_info(\n self, response: httpx.Response, *, with_file_path: bool = False\n ) -> tuple[bool, Path | None]:\n \"\"\"Determine the file path and whether the response content is binary.\n\n Args:\n response (Response): The HTTP response object.\n with_file_path (bool): Whether to save the response content to a file.\n\n Returns:\n Tuple[bool, Path | None]:\n A tuple containing a boolean indicating if the content is binary and the full file path (if applicable).\n \"\"\"\n content_type = response.headers.get(\"Content-Type\", \"\")\n is_binary = \"application/octet-stream\" in content_type or \"application/binary\" in content_type\n\n if not with_file_path:\n return is_binary, None\n\n component_temp_dir = Path(tempfile.gettempdir()) / self.__class__.__name__\n\n # Create directory asynchronously\n await aiofiles_os.makedirs(component_temp_dir, exist_ok=True)\n\n filename = None\n if \"Content-Disposition\" in response.headers:\n content_disposition = response.headers[\"Content-Disposition\"]\n filename_match = re.search(r'filename=\"(.+?)\"', content_disposition)\n if filename_match:\n extracted_filename = filename_match.group(1)\n filename = extracted_filename\n\n # Step 3: Infer file extension or use part of the request URL if no filename\n if not filename:\n # Extract the last segment of the URL path\n url_path = urlparse(str(response.request.url) if response.request else \"\").path\n base_name = Path(url_path).name # Get the last segment of the path\n if not base_name: # If the path ends with a slash or is empty\n base_name = \"response\"\n\n # Infer file extension\n content_type_to_extension = {\n \"text/plain\": \".txt\",\n \"application/json\": \".json\",\n \"image/jpeg\": \".jpg\",\n \"image/png\": \".png\",\n \"application/octet-stream\": \".bin\",\n }\n extension = content_type_to_extension.get(content_type, \".bin\" if is_binary else \".txt\")\n filename = f\"{base_name}{extension}\"\n\n # Step 4: Define the full file path\n file_path = component_temp_dir / filename\n\n # Step 5: Check if file exists asynchronously and handle accordingly\n try:\n # Try to create the file exclusively (x mode) to check existence\n async with aiofiles.open(file_path, \"x\") as _:\n pass # File created successfully, we can use this path\n except FileExistsError:\n # If file exists, append a timestamp to the filename\n timestamp = datetime.now(timezone.utc).strftime(\"%Y%m%d%H%M%S%f\")\n file_path = component_temp_dir / f\"{timestamp}-{filename}\"\n\n return is_binary, file_path\n" }, "curl_input": { "_input_type": "MultilineInput", diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json index f41b73142923..0a5100151b4d 100644 --- a/src/frontend/package-lock.json +++ b/src/frontend/package-lock.json @@ -125,15 +125,17 @@ } }, "node_modules/@adobe/css-tools": { - "version": "4.4.2", - "resolved": "https://registry.npmjs.org/@adobe/css-tools/-/css-tools-4.4.2.tgz", - "integrity": "sha512-baYZExFpsdkBNuvGKTKWCwKH57HRZLVtycZS05WTQNVOiXVSeAki3nU35zlRbToeMW8aHlJfyS+1C4BOv27q0A==", - "dev": true + "version": "4.4.3", + "resolved": "https://registry.npmjs.org/@adobe/css-tools/-/css-tools-4.4.3.tgz", + "integrity": "sha512-VQKMkwriZbaOgVCby1UDY/LDk5fIjhQicCvVPFqfe+69fWaPWydbWJ3wRt59/YzIwda1I81loas3oCoHxnqvdA==", + "dev": true, + "license": "MIT" }, "node_modules/@alloc/quick-lru": { "version": "5.2.0", "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz", "integrity": "sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw==", + "license": "MIT", "engines": { "node": ">=10" }, @@ -145,6 +147,7 @@ "version": "2.3.0", "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.3.0.tgz", "integrity": "sha512-30iZtAPgz+LTIYoeivqYo853f02jBYSd5uGnGpkFV0M3xOt9aN73erkgYAmZU43x4VfqcnLxW9Kpg3R5LC4YYw==", + "license": "Apache-2.0", "dependencies": { "@jridgewell/gen-mapping": "^0.3.5", "@jridgewell/trace-mapping": "^0.3.24" @@ -157,6 +160,7 @@ "version": "0.21.12", "resolved": "https://registry.npmjs.org/@antfu/ni/-/ni-0.21.12.tgz", "integrity": "sha512-2aDL3WUv8hMJb2L3r/PIQWsTLyq7RQr3v9xD16fiz6O8ys1xEyLhhTOv8gxtZvJiTzjTF5pHoArvRdesGL1DMQ==", + "license": "MIT", "bin": { "na": "bin/na.mjs", "nci": "bin/nci.mjs", @@ -171,6 +175,7 @@ "version": "1.0.0-rc.3", "resolved": "https://registry.npmjs.org/@axiomhq/js/-/js-1.0.0-rc.3.tgz", "integrity": "sha512-Zm10TczcMLounWqC42nMkXQ7XKLqjzLrd5ia022oBKDUZqAFVg2y9d1quQVNV4FlXyg9MKDdfMjpKQRmzEGaog==", + "license": "MIT", "dependencies": { "fetch-retry": "^6.0.0", "uuid": "^8.3.2" @@ -183,27 +188,30 @@ "version": "8.3.2", "resolved": "https://registry.npmjs.org/uuid/-/uuid-8.3.2.tgz", "integrity": "sha512-+NYs2QeMWy+GWFOEm9xnn6HCDp0l7QBD7ml8zLUmJ+93Q5NF0NocErnwkTkXVFNiX3/fpC6afS8Dhb/gz7R7eg==", + "license": "MIT", "bin": { "uuid": "dist/bin/uuid" } }, "node_modules/@babel/code-frame": { - "version": "7.26.2", - "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.26.2.tgz", - "integrity": "sha512-RJlIHRueQgwWitWgF8OdFYGZX328Ax5BCemNGlqHfplnRT9ESi8JkFlvaVYbS+UubVY6dpv87Fs2u5M29iNFVQ==", + "version": "7.27.1", + "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.27.1.tgz", + "integrity": "sha512-cjQ7ZlQ0Mv3b47hABuTevyTuYN4i+loJKGeV9flcCgIK37cCXRh+L1bd3iBHlynerhQ7BhCkn2BPbQUL+rGqFg==", + "license": "MIT", "dependencies": { - "@babel/helper-validator-identifier": "^7.25.9", + "@babel/helper-validator-identifier": "^7.27.1", "js-tokens": "^4.0.0", - "picocolors": "^1.0.0" + "picocolors": "^1.1.1" }, "engines": { "node": ">=6.9.0" } }, "node_modules/@babel/compat-data": { - "version": "7.26.8", - "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.26.8.tgz", - "integrity": "sha512-oH5UPLMWR3L2wEFLnFJ1TZXqHufiTKAiLfqw5zkhS4dKXLJ10yVztfil/twG8EDTA4F/tvVNw9nOl4ZMslB8rQ==", + "version": "7.27.5", + "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.27.5.tgz", + "integrity": "sha512-KiRAp/VoJaWkkte84TvUd9qjdbZAdiqyvMxrGl1N6vzFogKmaLgoM3L1kgtLicp2HP5fBJS8JrZKLVIZGVJAVg==", + "license": "MIT", "engines": { "node": ">=6.9.0" } @@ -212,6 +220,7 @@ "version": "7.26.0", "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.26.0.tgz", "integrity": "sha512-i1SLeK+DzNnQ3LL/CswPCa/E5u4lh1k6IAEphON8F+cXt0t9euTshDru0q7/IqMa1PMPz5RnHuHscF8/ZJsStg==", + "license": "MIT", "dependencies": { "@ampproject/remapping": "^2.2.0", "@babel/code-frame": "^7.26.0", @@ -240,23 +249,26 @@ "node_modules/@babel/core/node_modules/convert-source-map": { "version": "2.0.0", "resolved": "https://registry.npmjs.org/convert-source-map/-/convert-source-map-2.0.0.tgz", - "integrity": "sha512-Kvp459HrV2FEJ1CAsi1Ku+MY3kasH19TFykTz2xWmMeq6bk2NU3XXvfJ+Q61m0xktWwt+1HSYf3JZsTms3aRJg==" + "integrity": "sha512-Kvp459HrV2FEJ1CAsi1Ku+MY3kasH19TFykTz2xWmMeq6bk2NU3XXvfJ+Q61m0xktWwt+1HSYf3JZsTms3aRJg==", + "license": "MIT" }, "node_modules/@babel/core/node_modules/semver": { "version": "6.3.1", "resolved": "https://registry.npmjs.org/semver/-/semver-6.3.1.tgz", "integrity": "sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==", + "license": "ISC", "bin": { "semver": "bin/semver.js" } }, "node_modules/@babel/generator": { - "version": "7.27.0", - "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.27.0.tgz", - "integrity": 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"https://registry.npmjs.org/@babel/helper-compilation-targets/-/helper-compilation-targets-7.27.2.tgz", + "integrity": "sha512-2+1thGUUWWjLTYTHZWK1n8Yga0ijBz1XAhUXcKy81rd5g6yh7hGqMp45v7cadSbEHc9G3OTv45SyneRN3ps4DQ==", + "license": "MIT", "dependencies": { - "@babel/compat-data": "^7.26.8", - "@babel/helper-validator-option": "^7.25.9", + "@babel/compat-data": "^7.27.2", + "@babel/helper-validator-option": "^7.27.1", "browserslist": "^4.24.0", "lru-cache": "^5.1.1", "semver": "^6.3.1" @@ -284,30 +297,33 @@ "version": "6.3.1", "resolved": "https://registry.npmjs.org/semver/-/semver-6.3.1.tgz", "integrity": "sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==", + "license": "ISC", "bin": { "semver": "bin/semver.js" } }, "node_modules/@babel/helper-module-imports": { - "version": "7.25.9", - "resolved": "https://registry.npmjs.org/@babel/helper-module-imports/-/helper-module-imports-7.25.9.tgz", - "integrity": 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+2184,7 @@ "cpu": [ "x64" ], + "license": "MIT", "optional": true, "os": [ "android" @@ -2069,6 +2200,7 @@ "cpu": [ "arm64" ], + "license": "MIT", "optional": true, "os": [ "darwin" @@ -2084,6 +2216,7 @@ "cpu": [ "x64" ], + "license": "MIT", "optional": true, "os": [ "darwin" @@ -2099,6 +2232,7 @@ "cpu": [ "arm64" ], + "license": "MIT", "optional": true, "os": [ "freebsd" @@ -2114,6 +2248,7 @@ "cpu": [ "x64" ], + "license": "MIT", "optional": true, "os": [ "freebsd" @@ -2129,6 +2264,7 @@ "cpu": [ "arm" ], + "license": "MIT", "optional": true, "os": [ "linux" @@ -2144,6 +2280,7 @@ "cpu": [ "arm64" ], + "license": "MIT", "optional": true, "os": [ "linux" @@ -2159,6 +2296,7 @@ "cpu": [ "ia32" ], + "license": "MIT", "optional": true, "os": [ "linux" @@ -2174,6 +2312,7 @@ "cpu": [ "loong64" ], + "license": "MIT", "optional": true, "os": [ "linux" @@ -2189,6 +2328,7 @@ "cpu": [ "mips64el" ], + "license": "MIT", "optional": true, "os": [ "linux" @@ -2204,6 +2344,7 @@ "cpu": [ "ppc64" ], + "license": "MIT", "optional": true, "os": [ "linux" @@ -2219,6 +2360,7 @@ "cpu": [ "riscv64" ], + "license": "MIT", "optional": true, "os": [ "linux" @@ -2234,6 +2376,7 @@ "cpu": [ "s390x" ], + "license": "MIT", "optional": true, "os": [ "linux" @@ -2249,6 +2392,7 @@ "cpu": [ "x64" ], + "license": "MIT", "optional": true, "os": [ "linux" @@ -2264,6 +2408,7 @@ "cpu": [ "x64" ], + "license": "MIT", "optional": true, "os": [ "netbsd" @@ -2279,6 +2424,7 @@ "cpu": [ "x64" ], + "license": "MIT", "optional": true, "os": [ "openbsd" @@ -2294,6 +2440,7 @@ "cpu": [ "x64" ], + "license": "MIT", "optional": true, "os": [ "sunos" @@ -2309,6 +2456,7 @@ "cpu": [ "arm64" ], + "license": "MIT", "optional": true, "os": [ "win32" @@ -2324,6 +2472,7 @@ "cpu": [ "ia32" ], + "license": "MIT", "optional": true, "os": [ "win32" @@ -2339,6 +2488,7 @@ "cpu": [ "x64" ], + "license": "MIT", "optional": true, "os": [ "win32" @@ -2352,6 +2502,7 @@ "resolved": 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"optional": true, "os": [ "linux" @@ -2582,6 +2745,7 @@ "cpu": [ "riscv64" ], + "license": "MIT", "optional": true, "os": [ "linux" @@ -2597,6 +2761,7 @@ "cpu": [ "s390x" ], + "license": "MIT", "optional": true, "os": [ "linux" @@ -2612,6 +2777,7 @@ "cpu": [ "x64" ], + "license": "MIT", "optional": true, "os": [ "linux" @@ -2627,6 +2793,7 @@ "cpu": [ "x64" ], + "license": "MIT", "optional": true, "os": [ "linux" @@ -2642,6 +2809,7 @@ "cpu": [ "arm64" ], + "license": "MIT", "optional": true, "os": [ "win32" @@ -2657,6 +2825,7 @@ "cpu": [ "ia32" ], + "license": "MIT", "optional": true, "os": [ "win32" @@ -2672,6 +2841,7 @@ "cpu": [ "x64" ], + "license": "MIT", "optional": true, "os": [ "win32" @@ -2684,6 +2854,7 @@ "version": "2.1.5", "resolved": "https://registry.npmjs.org/@nodelib/fs.scandir/-/fs.scandir-2.1.5.tgz", "integrity": "sha512-vq24Bq3ym5HEQm2NKCr3yXDwjc7vTsEThRDnkp2DK9p1uqLR+DHurm/NOTo0KG7HYHU7eppKZj3MyqYuMBf62g==", + "license": "MIT", "dependencies": { "@nodelib/fs.stat": 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"1.0.2" @@ -3225,6 +3426,7 @@ "version": "1.0.2", "resolved": "https://registry.npmjs.org/@radix-ui/react-slot/-/react-slot-1.0.2.tgz", "integrity": "sha512-YeTpuq4deV+6DusvVUW4ivBgnkHwECUu0BiN43L5UCDFgdhsRUWAghhTF5MbvNTPzmiFOx90asDSUjWuCNapwg==", + "license": "MIT", "dependencies": { "@babel/runtime": "^7.13.10", "@radix-ui/react-compose-refs": "1.0.1" @@ -3243,6 +3445,7 @@ "version": "1.0.1", "resolved": "https://registry.npmjs.org/@radix-ui/react-use-layout-effect/-/react-use-layout-effect-1.0.1.tgz", "integrity": "sha512-v/5RegiJWYdoCvMnITBkNNx6bCj20fiaJnWtRkU18yITptraXjffz5Qbn05uOiQnOvi+dbkznkoaMltz1GnszQ==", + "license": "MIT", "dependencies": { "@babel/runtime": "^7.13.10" }, @@ -3260,6 +3463,7 @@ "version": "1.3.2", "resolved": "https://registry.npmjs.org/@radix-ui/react-icons/-/react-icons-1.3.2.tgz", "integrity": "sha512-fyQIhGDhzfc9pK2kH6Pl9c4BDJGfMkPqkyIgYDthyNYoNg3wVhoJMMh19WS4Up/1KMPFVpNsT2q3WmXn2N1m6g==", + "license": "MIT", "peerDependencies": { "react": "^16.x || ^17.x || ^18.x || ^19.0.0 || ^19.0.0-rc" } @@ -3268,6 +3472,7 @@ "version": "1.1.1", "resolved": "https://registry.npmjs.org/@radix-ui/react-id/-/react-id-1.1.1.tgz", "integrity": "sha512-kGkGegYIdQsOb4XjsfM97rXsiHaBwco+hFI66oO4s9LU+PLAC5oJ7khdOVFxkhsmlbpUqDAvXw11CluXP+jkHg==", + "license": "MIT", "dependencies": { "@radix-ui/react-use-layout-effect": "1.1.1" }, @@ -3282,11 +3487,12 @@ } }, "node_modules/@radix-ui/react-label": { - "version": "2.1.4", - "resolved": "https://registry.npmjs.org/@radix-ui/react-label/-/react-label-2.1.4.tgz", - "integrity": "sha512-wy3dqizZnZVV4ja0FNnUhIWNwWdoldXrneEyUcVtLYDAt8ovGS4ridtMAOGgXBBIfggL4BOveVWsjXDORdGEQg==", + "version": "2.1.7", + "resolved": "https://registry.npmjs.org/@radix-ui/react-label/-/react-label-2.1.7.tgz", + "integrity": "sha512-YT1GqPSL8kJn20djelMX7/cTRp/Y9w5IZHvfxQTVHrOqa2yMl7i/UfMqKRU5V7mEyKTrUVgJXhNQPVCG8PBLoQ==", + "license": "MIT", "dependencies": { - "@radix-ui/react-primitive": "2.1.0" + "@radix-ui/react-primitive": "2.1.3" }, "peerDependencies": { "@types/react": "*", @@ -3304,25 +3510,26 @@ } }, "node_modules/@radix-ui/react-menu": { - "version": "2.1.12", - "resolved": "https://registry.npmjs.org/@radix-ui/react-menu/-/react-menu-2.1.12.tgz", - "integrity": "sha512-+qYq6LfbiGo97Zz9fioX83HCiIYYFNs8zAsVCMQrIakoNYylIzWuoD/anAD3UzvvR6cnswmfRFJFq/zYYq/k7Q==", + "version": "2.1.15", + "resolved": "https://registry.npmjs.org/@radix-ui/react-menu/-/react-menu-2.1.15.tgz", + "integrity": "sha512-tVlmA3Vb9n8SZSd+YSbuFR66l87Wiy4du+YE+0hzKQEANA+7cWKH1WgqcEX4pXqxUFQKrWQGHdvEfw00TjFiew==", + "license": "MIT", "dependencies": { "@radix-ui/primitive": "1.1.2", - "@radix-ui/react-collection": "1.1.4", + "@radix-ui/react-collection": "1.1.7", "@radix-ui/react-compose-refs": "1.1.2", "@radix-ui/react-context": "1.1.2", "@radix-ui/react-direction": "1.1.1", - "@radix-ui/react-dismissable-layer": "1.1.7", + "@radix-ui/react-dismissable-layer": "1.1.10", "@radix-ui/react-focus-guards": "1.1.2", - "@radix-ui/react-focus-scope": 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@@ -3410,11 +3619,12 @@ } }, "node_modules/@radix-ui/react-portal": { - "version": "1.1.6", - "resolved": "https://registry.npmjs.org/@radix-ui/react-portal/-/react-portal-1.1.6.tgz", - "integrity": "sha512-XmsIl2z1n/TsYFLIdYam2rmFwf9OC/Sh2avkbmVMDuBZIe7hSpM0cYnWPAo7nHOVx8zTuwDZGByfcqLdnzp3Vw==", + "version": "1.1.9", + "resolved": "https://registry.npmjs.org/@radix-ui/react-portal/-/react-portal-1.1.9.tgz", + "integrity": "sha512-bpIxvq03if6UNwXZ+HTK71JLh4APvnXntDc6XOX8UVq4XQOVl7lwok0AvIl+b8zgCw3fSaVTZMpAPPagXbKmHQ==", + "license": "MIT", "dependencies": { - "@radix-ui/react-primitive": "2.1.0", + "@radix-ui/react-primitive": "2.1.3", "@radix-ui/react-use-layout-effect": "1.1.1" }, "peerDependencies": { @@ -3436,6 +3646,7 @@ "version": "1.1.4", "resolved": "https://registry.npmjs.org/@radix-ui/react-presence/-/react-presence-1.1.4.tgz", "integrity": "sha512-ueDqRbdc4/bkaQT3GIpLQssRlFgWaL/U2z/S31qRwwLWoxHLgry3SIfCwhxeQNbirEUXFa+lq3RL3oBYXtcmIA==", + "license": "MIT", "dependencies": { 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Do not use it. Check out lru-cache if you want a good and tested way to coalesce async requests by a key value, which is much more comprehensive and powerful.", + "license": "ISC", "optional": true, "peer": true, "dependencies": { @@ -9254,23 +9977,27 @@ "version": "2.0.4", "resolved": "https://registry.npmjs.org/inherits/-/inherits-2.0.4.tgz", "integrity": "sha512-k/vGaX4/Yla3WzyMCvTQOXYeIHvqOKtnqBduzTHpzpQZzAskKMhZ2K+EnBiSM9zGSoIFeMpXKxa4dYeZIQqewQ==", + "license": "ISC", "optional": true }, "node_modules/ini": { "version": "1.3.8", "resolved": "https://registry.npmjs.org/ini/-/ini-1.3.8.tgz", - "integrity": "sha512-JV/yugV2uzW5iMRSiZAyDtQd+nxtUnjeLt0acNdw98kKLrvuRVyB80tsREOE7yvGVgalhZ6RNXCmEHkUKBKxew==" + "integrity": "sha512-JV/yugV2uzW5iMRSiZAyDtQd+nxtUnjeLt0acNdw98kKLrvuRVyB80tsREOE7yvGVgalhZ6RNXCmEHkUKBKxew==", + "license": "ISC" }, "node_modules/inline-style-parser": { "version": "0.1.1", "resolved": "https://registry.npmjs.org/inline-style-parser/-/inline-style-parser-0.1.1.tgz", - "integrity": "sha512-7NXolsK4CAS5+xvdj5OMMbI962hU/wvwoxk+LWR9Ek9bVtyuuYScDN6eS0rUm6TxApFpw7CX1o4uJzcd4AyD3Q==" + "integrity": "sha512-7NXolsK4CAS5+xvdj5OMMbI962hU/wvwoxk+LWR9Ek9bVtyuuYScDN6eS0rUm6TxApFpw7CX1o4uJzcd4AyD3Q==", + "license": "MIT" }, "node_modules/inspect-with-kind": { "version": "1.0.5", "resolved": "https://registry.npmjs.org/inspect-with-kind/-/inspect-with-kind-1.0.5.tgz", "integrity": "sha512-MAQUJuIo7Xqk8EVNP+6d3CKq9c80hi4tjIbIAT6lmGW9W6WzlHiu9PS8uSuUYU+Do+j1baiFp3H25XEVxDIG2g==", "dev": true, + "license": "ISC", "dependencies": { "kind-of": "^6.0.2" } @@ -9279,6 +10006,7 @@ "version": "1.0.4", "resolved": "https://registry.npmjs.org/is-alphabetical/-/is-alphabetical-1.0.4.tgz", "integrity": "sha512-DwzsA04LQ10FHTZuL0/grVDk4rFoVH1pjAToYwBrHSxcrBIGQuXrQMtD5U1b0U2XVgKZCTLLP8u2Qxqhy3l2Vg==", + "license": "MIT", "funding": { "type": "github", "url": "https://github.com/sponsors/wooorm" @@ -9288,6 +10016,7 @@ "version": "1.0.4", "resolved": "https://registry.npmjs.org/is-alphanumerical/-/is-alphanumerical-1.0.4.tgz", "integrity": "sha512-UzoZUr+XfVz3t3v4KyGEniVL9BDRoQtY7tOyrRybkVNjDFWyo1yhXNGrrBTQxp3ib9BLAWs7k2YKBQsFRkZG9A==", + "license": "MIT", "dependencies": { "is-alphabetical": "^1.0.0", "is-decimal": "^1.0.0" @@ -9300,12 +10029,14 @@ "node_modules/is-arrayish": { "version": "0.2.1", "resolved": "https://registry.npmjs.org/is-arrayish/-/is-arrayish-0.2.1.tgz", - 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"^4.0.0", @@ -10435,6 +11269,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "decode-named-character-reference": "^1.0.0", "devlop": "^1.0.0", @@ -10468,6 +11303,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-character": "^2.0.0", "micromark-util-symbol": "^2.0.0", @@ -10488,6 +11324,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "devlop": "^1.0.0", "micromark-util-character": "^2.0.0", @@ -10509,6 +11346,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-character": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -10528,6 +11366,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-factory-space": "^2.0.0", "micromark-util-character": "^2.0.0", @@ -10549,6 +11388,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-factory-space": "^2.0.0", "micromark-util-character": "^2.0.0", @@ -10570,6 +11410,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -10589,6 +11430,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0" } @@ -10607,6 +11449,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-character": "^2.0.0", "micromark-util-symbol": "^2.0.0", @@ -10627,6 +11470,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-chunked": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -10646,6 +11490,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0" } @@ -10664,6 +11509,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "decode-named-character-reference": "^1.0.0", "micromark-util-character": "^2.0.0", @@ -10684,7 +11530,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/mdast-util-math/node_modules/micromark-util-html-tag-name": { "version": "2.0.1", @@ -10699,7 +11546,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/mdast-util-math/node_modules/micromark-util-normalize-identifier": { "version": "2.0.1", @@ -10715,6 +11563,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0" } @@ -10733,6 +11582,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-types": "^2.0.0" } @@ -10751,6 +11601,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-character": "^2.0.0", "micromark-util-encode": "^2.0.0", @@ -10771,6 +11622,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "devlop": "^1.0.0", "micromark-util-chunked": "^2.0.0", @@ -10791,7 +11643,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/mdast-util-math/node_modules/micromark-util-types": { "version": "2.0.2", @@ -10806,12 +11659,14 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/mdast-util-math/node_modules/unist-util-is": { "version": "6.0.0", "resolved": "https://registry.npmjs.org/unist-util-is/-/unist-util-is-6.0.0.tgz", "integrity": "sha512-2qCTHimwdxLfz+YzdGfkqNlH0tLi9xjTnHddPmJwtIG9MGsdbutfTc4P+haPD7l7Cjxf/WZj+we5qfVPvvxfYw==", + "license": "MIT", "dependencies": { "@types/unist": "^3.0.0" }, @@ -10824,6 +11679,7 @@ "version": "4.0.0", "resolved": 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}, "node_modules/simple-get": { @@ -14194,6 +15302,7 @@ "url": "https://feross.org/support" } ], + "license": "MIT", "optional": true, "dependencies": { "decompress-response": "^6.0.0", @@ -14207,6 +15316,7 @@ "integrity": "sha512-N+goiLxlkHJlyaYEglFypzVNMaNplPAk5syu0+OPp/Bk6dwVoXF6FfOw2vO0Dp+JHsBaI+w6cm8TnFl2Hw6tDA==", "dev": true, "hasInstallScript": true, + "license": "MIT", "bin": { "simple-git-hooks": "cli.js" } @@ -14214,13 +15324,15 @@ "node_modules/sisteransi": { "version": "1.0.5", "resolved": "https://registry.npmjs.org/sisteransi/-/sisteransi-1.0.5.tgz", - "integrity": "sha512-bLGGlR1QxBcynn2d5YmDX4MGjlZvy2MRBDRNHLJ8VI6l6+9FUiyTFNJ0IveOSP0bcXgVDPRcfGqA0pjaqUpfVg==" + "integrity": "sha512-bLGGlR1QxBcynn2d5YmDX4MGjlZvy2MRBDRNHLJ8VI6l6+9FUiyTFNJ0IveOSP0bcXgVDPRcfGqA0pjaqUpfVg==", + "license": "MIT" }, "node_modules/slash": { "version": "3.0.0", "resolved": "https://registry.npmjs.org/slash/-/slash-3.0.0.tgz", "integrity": 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forwardRef } from "react"; +import SvgCleanlab from "./Cleanlab"; + +export const CleanlabIcon = forwardRef< + SVGSVGElement, + React.PropsWithChildren<{}> +>((props, ref) => { + const isdark = useDarkStore((state) => state.dark).toString(); + return ; +}); diff --git a/src/frontend/src/icons/lazyIconImports.ts b/src/frontend/src/icons/lazyIconImports.ts index 8d0371306413..72947c66afba 100644 --- a/src/frontend/src/icons/lazyIconImports.ts +++ b/src/frontend/src/icons/lazyIconImports.ts @@ -42,6 +42,8 @@ export const lazyIconsMapping = { import("@/icons/Cassandra").then((mod) => ({ default: mod.CassandraIcon })), Chroma: () => import("@/icons/ChromaIcon").then((mod) => ({ default: mod.ChromaIcon })), + Cleanlab: () => + import("@/icons/Cleanlab").then((mod) => ({ default: mod.CleanlabIcon })), Clickhouse: () => import("@/icons/Clickhouse").then((mod) => ({ default: mod.ClickhouseIcon, diff --git a/src/frontend/src/utils/styleUtils.ts b/src/frontend/src/utils/styleUtils.ts index 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