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{
"data": {
"edges": [
{
"animated": false,
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"data": {
"sourceHandle": {
"dataType": "CalculatorTool",
"id": "CalculatorTool-DF8xQ",
"name": "api_build_tool",
"output_types": [
"Tool"
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},
"targetHandle": {
"fieldName": "tools",
"id": "Agent-5e01q",
"inputTypes": [
"Tool",
"BaseTool",
"StructuredTool"
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"source": "CalculatorTool-DF8xQ",
"sourceHandle": "{œdataTypeœ: œCalculatorToolœ, œidœ: œCalculatorTool-DF8xQœ, œnameœ: œapi_build_toolœ, œoutput_typesœ: [œToolœ]}",
"target": "Agent-5e01q",
"targetHandle": "{œfieldNameœ: œtoolsœ, œidœ: œAgent-5e01qœ, œinputTypesœ: [œToolœ, œBaseToolœ, œStructuredToolœ], œtypeœ: œotherœ}"
},
{
"animated": false,
"className": "",
"data": {
"sourceHandle": {
"dataType": "Agent",
"id": "Agent-5e01q",
"name": "response",
"output_types": [
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]
},
"targetHandle": {
"fieldName": "input_value",
"id": "ChatOutput-s1eJK",
"inputTypes": [
"Message"
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},
"id": "reactflow__edge-Agent-5e01q{œdataTypeœ:œAgentœ,œidœ:œAgent-5e01qœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-s1eJK{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-s1eJKœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
"source": "Agent-5e01q",
"sourceHandle": "{œdataTypeœ: œAgentœ, œidœ: œAgent-5e01qœ, œnameœ: œresponseœ, œoutput_typesœ: [œMessageœ]}",
"target": "ChatOutput-s1eJK",
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-s1eJKœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
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"dataType": "Prompt",
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"id": "reactflow__edge-Prompt-KkcsZ{œdataTypeœ:œPromptœ,œidœ:œPrompt-KkcsZœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-Agent-5e01q{œfieldNameœ:œinput_valueœ,œidœ:œAgent-5e01qœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
"source": "Prompt-KkcsZ",
"sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-KkcsZœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
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"nodes": [
{
"data": {
"description": "Create a prompt template with dynamic variables.",
"display_name": "Prompt",
"id": "Prompt-KkcsZ",
"node": {
"base_classes": [
"Message"
],
"beta": false,
"conditional_paths": [],
"custom_fields": {
"template": [
"monthly_infrastructure_costs",
"customer_support_cost",
"continuous_development_cost",
"desired_profit_margin",
"estimated_subscribers"
]
},
"description": "Create a prompt template with dynamic variables.",
"display_name": "Prompt",
"documentation": "",
"edited": false,
"field_order": [
"template"
],
"frozen": false,
"icon": "prompts",
"legacy": false,
"lf_version": "1.0.19.post2",
"metadata": {},
"output_types": [],
"outputs": [
{
"cache": true,
"display_name": "Prompt Message",
"method": "build_prompt",
"name": "prompt",
"selected": "Message",
"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.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 def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n \"\"\"This function is called after the code validation is done.\"\"\"\n frontend_node = super().post_code_processing(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"
},
"continuous_development_cost": {
"advanced": false,
"display_name": "continuous_development_cost",
"dynamic": false,
"field_type": "str",
"fileTypes": [],
"file_path": "",
"info": "",
"input_types": [
"Message",
"Text"
],
"list": false,
"load_from_db": false,
"multiline": true,
"name": "continuous_development_cost",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"type": "str",
"value": "3000"
},
"customer_support_cost": {
"advanced": false,
"display_name": "customer_support_cost",
"dynamic": false,
"field_type": "str",
"fileTypes": [],
"file_path": "",
"info": "",
"input_types": [
"Message",
"Text"
],
"list": false,
"load_from_db": false,
"multiline": true,
"name": "customer_support_cost",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"type": "str",
"value": "1000"
},
"desired_profit_margin": {
"advanced": false,
"display_name": "desired_profit_margin",
"dynamic": false,
"field_type": "str",
"fileTypes": [],
"file_path": "",
"info": "",
"input_types": [
"Message",
"Text"
],
"list": false,
"load_from_db": false,
"multiline": true,
"name": "desired_profit_margin",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"type": "str",
"value": "30"
},
"estimated_subscribers": {
"advanced": false,
"display_name": "estimated_subscribers",
"dynamic": false,
"field_type": "str",
"fileTypes": [],
"file_path": "",
"info": "",
"input_types": [
"Message",
"Text"
],
"list": false,
"load_from_db": false,
"multiline": true,
"name": "estimated_subscribers",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"type": "str",
"value": "200"
},
"monthly_infrastructure_costs": {
"advanced": false,
"display_name": "monthly_infrastructure_costs",
"dynamic": false,
"field_type": "str",
"fileTypes": [],
"file_path": "",
"info": "",
"input_types": [
"Message",
"Text"
],
"list": false,
"load_from_db": false,
"multiline": true,
"name": "monthly_infrastructure_costs",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"type": "str",
"value": "2000"
},
"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": "To calculate the monthly subscription price of the software based on the following data:\n\nMonthly infrastructure costs: ${monthly_infrastructure_costs}\nCustomer support: ${customer_support_cost}\nContinuous development: {continuous_development_cost}\nDesired profit margin: {desired_profit_margin}%\nEstimated number of subscribers: {estimated_subscribers}\n\nFollow the step to formulate the answer:\nFixed costs:\nProfit margin:\nTotal amount needed:\nPrice per subscriber:\nThe minimum subscription price per subscriber is:"
},
"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": 693,
"id": "Prompt-KkcsZ",
"position": {
"x": 1349.861745038984,
"y": 347.90475109976467
},
"positionAbsolute": {
"x": 1349.861745038984,
"y": 347.90475109976467
},
"selected": false,
"type": "genericNode",
"width": 320
},
{
"data": {
"description": "Display a chat message in the Playground.",
"display_name": "Chat Output",
"id": "ChatOutput-s1eJK",
"node": {
"base_classes": [
"Message"
],
"beta": false,
"conditional_paths": [],
"custom_fields": {},
"description": "Display a chat message in the Playground.",
"display_name": "Chat Output",
"documentation": "",
"edited": false,
"field_order": [
"input_value",
"should_store_message",
"sender",
"sender_name",
"session_id",
"data_template",
"background_color",
"chat_icon",
"text_color"
],
"frozen": false,
"icon": "MessagesSquare",
"legacy": false,
"lf_version": "1.0.19.post2",
"metadata": {},
"output_types": [],
"outputs": [
{
"cache": true,
"display_name": "Message",
"method": "message_response",
"name": "message",
"selected": "Message",
"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,
"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,
"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": ""
},
"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.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\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\n inputs = [\n MessageInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Message to be passed as output.\",\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 ]\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 source_dict[\"source\"] = source\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\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 message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\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 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"
},
"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,
"load_from_db": false,
"name": "data_template",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_input": true,
"trace_as_metadata": true,
"type": "str",
"value": "{text}"
},
"input_value": {
"_input_type": "MessageInput",
"advanced": false,
"display_name": "Text",
"dynamic": false,
"info": "Message to be passed as output.",
"input_types": [
"Message"
],
"list": false,
"load_from_db": false,
"name": "input_value",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"trace_as_input": true,
"trace_as_metadata": true,
"type": "str",
"value": ""
},
"sender": {
"_input_type": "DropdownInput",
"advanced": true,
"combobox": false,
"display_name": "Sender Type",
"dynamic": false,
"info": "Type of sender.",
"name": "sender",
"options": [
"Machine",
"User"
],
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "str",
"value": "Machine"
},
"sender_name": {
"_input_type": "MessageTextInput",
"advanced": true,
"display_name": "Sender Name",
"dynamic": false,
"info": "Name of the sender.",
"input_types": [
"Message"
],
"list": false,
"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,
"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,
"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,
"tool_mode": false,
"trace_as_input": true,
"trace_as_metadata": true,
"type": "str",
"value": ""
}
},
"tool_mode": false
},
"type": "ChatOutput"
},
"dragging": false,
"height": 234,
"id": "ChatOutput-s1eJK",
"position": {
"x": 2240.3625274769397,
"y": 355.16302699218204
},
"positionAbsolute": {
"x": 2240.3625274769397,
"y": 355.16302699218204
},
"selected": false,
"type": "genericNode",
"width": 320
},
{
"data": {
"id": "note-H6OpG",
"node": {
"description": "# SaaS Pricing Calculator\n\nWelcome to the SaaS Pricing Calculator! This flow helps you determine the optimal monthly subscription price for your software service.\n\n## Instructions\n\n1. Prepare Your Data\n - Gather information on monthly infrastructure costs\n - Calculate customer support expenses\n - Estimate continuous development costs\n - Decide on your desired profit margin\n - Determine the estimated number of subscribers\n\n2. Input Values\n - Enter the gathered data into the respective fields in the Prompt node\n - Double-check the accuracy of your inputs\n\n3. Run the Flow\n - Click the \"Run\" button to start the calculation process\n - The flow will use Chain-of-Thought prompting to guide the AI through the steps\n\n4. Review the Results\n - Examine the output in the Chat Output node\n - The result will show a breakdown of costs and the final subscription price\n\n5. Adjust and Refine\n - If needed, modify your inputs to explore different pricing scenarios\n - Re-run the flow to see how changes affect the final price\n\nRemember: Regularly update your costs and subscriber estimates to keep your pricing model accurate and competitive! 💼📊",
"display_name": "",
"documentation": "",
"template": {}
},
"type": "note"
},
"dragging": false,
"height": 800,
"id": "note-H6OpG",
"position": {
"x": 689.7659055360411,
"y": 68.95847391680593
},
"positionAbsolute": {
"x": 689.7659055360411,
"y": 68.95847391680593
},
"resizing": false,
"selected": false,
"style": {
"height": 800,
"width": 600
},
"type": "noteNode",
"width": 600
},
{
"data": {
"description": "Define the agent's instructions, then enter a task to complete using tools.",
"display_name": "Agent",
"id": "Agent-5e01q",
"node": {
"base_classes": [
"Message"
],
"beta": false,
"conditional_paths": [],
"custom_fields": {},
"description": "Define the agent's instructions, then enter a task to complete using tools.",
"display_name": "Agent",
"documentation": "",
"edited": false,
"field_order": [
"agent_llm",
"max_tokens",
"model_kwargs",
"json_mode",
"output_schema",
"model_name",
"openai_api_base",
"api_key",
"temperature",
"seed",
"output_parser",
"system_prompt",
"tools",
"input_value",
"handle_parsing_errors",
"verbose",
"max_iterations",
"agent_description",
"memory",
"sender",
"sender_name",
"n_messages",
"session_id",
"order",
"template",
"add_current_date_tool"
],
"frozen": false,
"icon": "bot",
"legacy": false,
"lf_version": "1.0.19.post2",
"metadata": {},
"output_types": [],
"outputs": [
{
"cache": true,
"display_name": "Response",
"method": "message_response",
"name": "response",
"selected": "Message",
"types": [
"Message"
],
"value": "__UNDEFINED__"
}
],
"pinned": false,
"template": {
"_type": "Component",
"add_current_date_tool": {
"_input_type": "BoolInput",
"advanced": true,
"display_name": "Current Date",
"dynamic": false,
"info": "If true, will add a tool to the agent that returns the current date.",
"list": false,
"name": "add_current_date_tool",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "bool",
"value": true
},
"agent_description": {
"_input_type": "MultilineInput",
"advanced": true,
"display_name": "Agent Description",
"dynamic": false,
"info": "The description of the agent. This is only used when in Tool Mode. Defaults to 'A helpful assistant with access to the following tools:' and tools are added dynamically.",
"input_types": [
"Message"
],
"list": false,
"load_from_db": false,
"multiline": true,
"name": "agent_description",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_input": true,
"trace_as_metadata": true,
"type": "str",
"value": "A helpful assistant with access to the following tools:"
},
"agent_llm": {
"_input_type": "DropdownInput",
"advanced": false,
"combobox": false,
"display_name": "Model Provider",
"dynamic": false,
"info": "The provider of the language model that the agent will use to generate responses.",
"input_types": [],
"name": "agent_llm",
"options": [
"Amazon Bedrock",
"Anthropic",
"Azure OpenAI",
"Groq",
"NVIDIA",
"OpenAI",
"Custom"
],
"placeholder": "",
"real_time_refresh": true,
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "str",
"value": "OpenAI"
},
"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": [
"Message"
],
"load_from_db": false,
"name": "api_key",
"password": true,
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"type": "str",
"value": "OPENAI_API_KEY"
},
"code": {
"advanced": true,
"dynamic": true,
"fileTypes": [],
"file_path": "",
"info": "",
"list": false,
"load_from_db": false,
"multiline": true,
"name": "code",
"password": false,
"placeholder": "",
"required": true,
"show": true,
"title_case": false,
"type": "code",
"value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n ALL_PROVIDER_FIELDS,\n MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n DropdownInput(\n name=\"agent_llm\",\n display_name=\"Model Provider\",\n info=\"The provider of the language model that the agent will use to generate responses.\",\n options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n value=\"OpenAI\",\n real_time_refresh=True,\n input_types=[],\n ),\n *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n advanced=False,\n ),\n *LCToolsAgentComponent._base_inputs,\n *memory_inputs,\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n ]\n outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n async def message_response(self) -> Message:\n llm_model, display_name = self.get_llm()\n self.model_name = get_model_name(llm_model, display_name=display_name)\n if llm_model is None:\n msg = \"No language model selected\"\n raise ValueError(msg)\n self.chat_history = await self.get_memory_data()\n\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n # Convert CurrentDateComponent to a StructuredTool\n current_date_tool = CurrentDateComponent().to_toolkit()[0]\n if isinstance(current_date_tool, StructuredTool):\n self.tools.append(current_date_tool)\n else:\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise ValueError(msg)\n\n if not self.tools:\n msg = \"Tools are required to run the agent.\"\n raise ValueError(msg)\n self.set(\n llm=llm_model,\n tools=self.tools,\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self.system_prompt,\n )\n agent = self.create_agent_runnable()\n return await self.run_agent(agent)\n\n async def get_memory_data(self):\n memory_kwargs = {\n component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n }\n\n return await MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n def get_llm(self):\n if isinstance(self.agent_llm, str):\n try:\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n display_name = component_class.display_name\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\", \"\")\n return (\n self._build_llm_model(component_class, inputs, prefix),\n display_name,\n )\n except Exception as e:\n msg = f\"Error building {self.agent_llm} language model\"\n raise ValueError(msg) from e\n return self.agent_llm, None\n\n def _build_llm_model(self, component, inputs, prefix=\"\"):\n model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n return component.set(**model_kwargs).build_model()\n\n def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n \"\"\"Delete specified fields from build_config.\"\"\"\n for field in fields:\n build_config.pop(field, None)\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n # Iterate over all providers in the MODEL_PROVIDERS_DICT\n # Existing logic for updating build_config\n if field_name == \"agent_llm\":\n provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n # Call the component class's update_build_config method\n build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n provider_configs: dict[str, tuple[dict, list[dict]]] = {\n provider: (\n MODEL_PROVIDERS_DICT[provider][\"fields\"],\n [\n MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n for other_provider in MODEL_PROVIDERS_DICT\n if other_provider != provider\n ],\n )\n for provider in MODEL_PROVIDERS_DICT\n }\n if field_value in provider_configs:\n fields_to_add, fields_to_delete = provider_configs[field_value]\n\n # Delete fields from other providers\n for fields in fields_to_delete:\n self.delete_fields(build_config, fields)\n\n # Add provider-specific fields\n if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n build_config.update(fields_to_add)\n else:\n build_config.update(fields_to_add)\n # Reset input types for agent_llm\n build_config[\"agent_llm\"][\"input_types\"] = []\n elif field_value == \"Custom\":\n # Delete all provider fields\n self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n # Update with custom component\n custom_component = DropdownInput(\n name=\"agent_llm\",\n display_name=\"Language Model\",\n options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n value=\"Custom\",\n real_time_refresh=True,\n input_types=[\"LanguageModel\"],\n )\n build_config.update({\"agent_llm\": custom_component.to_dict()})\n # Update input types for all fields\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"agent_llm\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n prefix = provider_info.get(\"prefix\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n # Call each component class's update_build_config method\n # remove the prefix from the field_name\n if isinstance(field_name, str) and isinstance(prefix, str):\n field_name = field_name.replace(prefix, \"\")\n build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n return build_config\n"
},
"handle_parsing_errors": {
"_input_type": "BoolInput",
"advanced": true,
"display_name": "Handle Parse Errors",
"dynamic": false,
"info": "Should the Agent fix errors when reading user input for better processing?",
"list": false,
"name": "handle_parsing_errors",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "bool",
"value": true
},
"input_value": {
"_input_type": "MessageTextInput",
"advanced": false,
"display_name": "Input",
"dynamic": false,
"info": "The input provided by the user for the agent to process.",
"input_types": [
"Message"
],
"list": false,
"load_from_db": false,
"name": "input_value",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": true,
"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,
"name": "json_mode",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "bool",
"value": false
},
"max_iterations": {
"_input_type": "IntInput",
"advanced": true,
"display_name": "Max Iterations",
"dynamic": false,
"info": "The maximum number of attempts the agent can make to complete its task before it stops.",
"list": false,
"name": "max_iterations",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "int",
"value": 15
},
"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,
"name": "max_tokens",
"placeholder": "",
"range_spec": {
"max": 128000,
"min": 0,
"step": 0.1,
"step_type": "float"
},
"required": false,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "int",
"value": ""
},
"memory": {
"_input_type": "HandleInput",
"advanced": true,
"display_name": "External Memory",
"dynamic": false,
"info": "Retrieve messages from an external memory. If empty, it will use the Langflow tables.",
"input_types": [
"BaseChatMessageHistory"
],
"list": false,
"name": "memory",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "other",
"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,
"name": "model_kwargs",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"trace_as_input": true,
"type": "dict",
"value": {}
},
"model_name": {
"_input_type": "DropdownInput",
"advanced": false,
"combobox": false,
"display_name": "Model Name",
"dynamic": false,
"info": "",
"name": "model_name",
"options": [
"gpt-4o-mini",
"gpt-4o",
"gpt-4-turbo",
"gpt-4-turbo-preview",
"gpt-4",
"gpt-3.5-turbo",
"gpt-3.5-turbo-0125"
],
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "str",
"value": "gpt-4o-mini"
},
"n_messages": {
"_input_type": "IntInput",
"advanced": true,
"display_name": "Number of Messages",
"dynamic": false,
"info": "Number of messages to retrieve.",
"list": false,
"name": "n_messages",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "int",
"value": 100
},
"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,
"load_from_db": false,
"name": "openai_api_base",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "str",
"value": ""
},
"order": {
"_input_type": "DropdownInput",
"advanced": true,
"combobox": false,
"display_name": "Order",
"dynamic": false,
"info": "Order of the messages.",
"name": "order",
"options": [
"Ascending",
"Descending"
],
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "str",
"value": "Ascending"
},
"output_parser": {
"_input_type": "HandleInput",
"advanced": true,
"display_name": "Output Parser",
"dynamic": false,
"info": "The parser to use to parse the output of the model",
"input_types": [
"OutputParser"
],
"list": false,
"name": "output_parser",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "other",
"value": ""
},
"output_schema": {
"_input_type": "DictInput",
"advanced": true,
"display_name": "Schema",
"dynamic": false,
"info": "The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled. [DEPRECATED]",
"list": true,
"name": "output_schema",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"trace_as_input": true,
"type": "dict",
"value": {}
},
"seed": {
"_input_type": "IntInput",
"advanced": true,
"display_name": "Seed",
"dynamic": false,
"info": "The seed controls the reproducibility of the job.",
"list": false,
"name": "seed",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "int",
"value": 1
},
"sender": {
"_input_type": "DropdownInput",
"advanced": true,
"combobox": false,
"display_name": "Sender Type",
"dynamic": false,
"info": "Filter by sender type.",
"name": "sender",
"options": [
"Machine",
"User",
"Machine and User"
],
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "str",
"value": "Machine and User"
},
"sender_name": {
"_input_type": "MessageTextInput",
"advanced": true,
"display_name": "Sender Name",
"dynamic": false,
"info": "Filter by sender name.",
"input_types": [
"Message"
],
"list": false,
"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": ""
},
"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,
"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": ""
},
"system_prompt": {
"_input_type": "MultilineInput",
"advanced": false,
"display_name": "Agent Instructions",
"dynamic": false,
"info": "System Prompt: Initial instructions and context provided to guide the agent's behavior.",
"input_types": [
"Message"
],
"list": false,
"load_from_db": false,
"multiline": true,
"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": "# Subscription Pricing Calculator\n\n## Purpose\nCalculate the optimal monthly subscription price for a software product based on operational costs, desired profit margin, and estimated subscriber base.\n\n## Input Variables\nThe system requires the following inputs:\n- Monthly infrastructure costs (numeric)\n- Customer support costs (numeric)\n- Continuous development costs (numeric)\n- Desired profit margin (percentage)\n- Estimated number of subscribers (numeric)\n\n## Calculation Process\nFollow these steps to determine the subscription price:\n\n### Step 1: Total Monthly Costs\nCalculate the sum of all fixed operational costs:\n```\ntotal_monthly_costs = infrastructure_costs + support_costs + development_costs\n```\n\n### Step 2: Profit Margin Calculation\nCalculate the profit margin amount based on total costs:\n```\nprofit_amount = total_monthly_costs × (profit_margin_percentage / 100)\n```\n\n### Step 3: Total Revenue Required\nCalculate the total monthly revenue needed:\n```\ntotal_revenue_needed = total_monthly_costs + profit_amount\n```\n\n### Step 4: Per-Subscriber Price\nCalculate the minimum price per subscriber:\n```\nsubscription_price = total_revenue_needed ÷ estimated_subscribers\n```\n\n## Output Format\nPresent the results in the following structure:\n\nFixed costs: [sum of all costs]\nProfit margin: [calculated profit amount]\nTotal amount needed: [total revenue required]\nPrice per subscriber: [calculated subscription price]\n\nFinal recommendation: \"The minimum subscription price per subscriber should be [price] to achieve the desired profit margin of [percentage]%\"\n\n## Notes\n- All monetary values should be rounded to 2 decimal places\n- Ensure all input values are positive numbers\n- Validate that the estimated subscribers count is greater than zero\n- The profit margin percentage should be between 0 and 100"
},
"temperature": {
"_input_type": "FloatInput",
"advanced": true,
"display_name": "Temperature",
"dynamic": false,
"info": "",
"list": false,
"name": "temperature",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "float",
"value": 0.1
},
"template": {
"_input_type": "MultilineInput",
"advanced": true,
"display_name": "Template",
"dynamic": false,
"info": "The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.",
"input_types": [
"Message"
],
"list": false,
"load_from_db": false,
"multiline": true,
"name": "template",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_input": true,
"trace_as_metadata": true,
"type": "str",
"value": "{sender_name}: {text}"
},
"tools": {
"_input_type": "HandleInput",
"advanced": false,
"display_name": "Tools",
"dynamic": false,
"info": "These are the tools that the agent can use to help with tasks.",
"input_types": [
"Tool",
"BaseTool",
"StructuredTool"
],
"list": true,
"name": "tools",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "other",
"value": ""
},
"verbose": {
"_input_type": "BoolInput",
"advanced": true,
"display_name": "Verbose",
"dynamic": false,
"info": "",
"list": false,
"name": "verbose",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "bool",
"value": true
}
},
"tool_mode": false
},
"type": "Agent"
},
"dragging": false,
"height": 650,
"id": "Agent-5e01q",
"position": {
"x": 1819.2633856623966,
"y": 138.32023808479687
},
"positionAbsolute": {
"x": 1819.2633856623966,
"y": 138.32023808479687
},
"selected": false,
"type": "genericNode",
"width": 320
},
{
"data": {
"id": "CalculatorTool-DF8xQ",
"node": {
"base_classes": [
"Data",
"Tool"
],
"beta": false,
"conditional_paths": [],
"custom_fields": {},
"description": "Perform basic arithmetic operations on a given expression.",
"display_name": "Calculator",
"documentation": "",
"edited": false,
"field_order": [
"expression"
],
"frozen": false,
"icon": "calculator",
"legacy": false,
"lf_version": "1.0.19.post2",
"metadata": {},
"output_types": [],
"outputs": [
{
"cache": true,
"display_name": "Data",
"method": "run_model",
"name": "api_run_model",
"required_inputs": [],
"selected": "Data",
"types": [
"Data"
],
"value": "__UNDEFINED__"
},
{
"cache": true,
"display_name": "Tool",
"method": "build_tool",
"name": "api_build_tool",
"required_inputs": [],
"selected": "Tool",
"types": [
"Tool"
],
"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": "import ast\nimport operator\n\nfrom langchain.tools import StructuredTool\nfrom langchain_core.tools import ToolException\nfrom loguru import logger\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import MessageTextInput\nfrom langflow.schema import Data\n\n\nclass CalculatorToolComponent(LCToolComponent):\n display_name = \"Calculator\"\n description = \"Perform basic arithmetic operations on a given expression.\"\n icon = \"calculator\"\n name = \"CalculatorTool\"\n\n inputs = [\n MessageTextInput(\n name=\"expression\",\n display_name=\"Expression\",\n info=\"The arithmetic expression to evaluate (e.g., '4*4*(33/22)+12-20').\",\n ),\n ]\n\n class CalculatorToolSchema(BaseModel):\n expression: str = Field(..., description=\"The arithmetic expression to evaluate.\")\n\n def run_model(self) -> list[Data]:\n return self._evaluate_expression(self.expression)\n\n def build_tool(self) -> Tool:\n return StructuredTool.from_function(\n name=\"calculator\",\n description=\"Evaluate basic arithmetic expressions. Input should be a string containing the expression.\",\n func=self._eval_expr_with_error,\n args_schema=self.CalculatorToolSchema,\n )\n\n def _eval_expr(self, node):\n # Define the allowed operators\n operators = {\n ast.Add: operator.add,\n ast.Sub: operator.sub,\n ast.Mult: operator.mul,\n ast.Div: operator.truediv,\n ast.Pow: operator.pow,\n }\n if isinstance(node, ast.Num):\n return node.n\n if isinstance(node, ast.BinOp):\n return operators[type(node.op)](self._eval_expr(node.left), self._eval_expr(node.right))\n if isinstance(node, ast.UnaryOp):\n return operators[type(node.op)](self._eval_expr(node.operand))\n if isinstance(node, ast.Call):\n msg = (\n \"Function calls like sqrt(), sin(), cos() etc. are not supported. \"\n \"Only basic arithmetic operations (+, -, *, /, **) are allowed.\"\n )\n raise TypeError(msg)\n msg = f\"Unsupported operation or expression type: {type(node).__name__}\"\n raise TypeError(msg)\n\n def _eval_expr_with_error(self, expression: str) -> list[Data]:\n try:\n return self._evaluate_expression(expression)\n except Exception as e:\n raise ToolException(str(e)) from e\n\n def _evaluate_expression(self, expression: str) -> list[Data]:\n try:\n # Parse the expression and evaluate it\n tree = ast.parse(expression, mode=\"eval\")\n result = self._eval_expr(tree.body)\n\n # Format the result to a reasonable number of decimal places\n formatted_result = f\"{result:.6f}\".rstrip(\"0\").rstrip(\".\")\n\n self.status = formatted_result\n return [Data(data={\"result\": formatted_result})]\n\n except (SyntaxError, TypeError, KeyError) as e:\n error_message = f\"Invalid expression: {e}\"\n self.status = error_message\n return [Data(data={\"error\": error_message, \"input\": expression})]\n except ZeroDivisionError:\n error_message = \"Error: Division by zero\"\n self.status = error_message\n return [Data(data={\"error\": error_message, \"input\": expression})]\n except Exception as e: # noqa: BLE001\n logger.opt(exception=True).debug(\"Error evaluating expression\")\n error_message = f\"Error: {e}\"\n self.status = error_message\n return [Data(data={\"error\": error_message, \"input\": expression})]\n"
},
"expression": {
"_input_type": "MessageTextInput",
"advanced": false,
"display_name": "Expression",
"dynamic": false,
"info": "The arithmetic expression to evaluate (e.g., '4*4*(33/22)+12-20').",
"input_types": [
"Message"
],
"list": false,
"load_from_db": false,
"name": "expression",
"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": "CalculatorTool"
},
"dragging": false,
"height": 167,
"id": "CalculatorTool-DF8xQ",
"position": {
"x": 1347.154214046272,
"y": 28.770424745017564
},
"positionAbsolute": {
"x": 1347.154214046272,
"y": 28.770424745017564
},
"selected": false,
"type": "genericNode",
"width": 320
}
],
"viewport": {
"x": -251.17743782763955,
"y": 134.52045967838717,
"zoom": 0.6368650431844803
}
},
"description": "starterProjects.saasPricing.description",
"endpoint_name": null,
"gradient": "3",
"icon": "calculator",
"id": "9357f72e-2121-4541-8e7d-74b7ba2ada2b",
"is_component": false,
"last_tested_version": "1.0.19.post2",
"name": "starterProjects.saasPricing.name",
"tags": [
"agents",
"assistants"
]
}