{ "data": { "edges": [ { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "OpenAIModel", "id": "OpenAIModel-Rc3MO", "name": "text_output", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "previous_response", "id": "Prompt-u7GZR", "inputTypes": [ "Message", "Text" ], "type": "str" } }, "id": "reactflow__edge-OpenAIModel-Rc3MO{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-Rc3MOœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-Prompt-u7GZR{œfieldNameœ:œprevious_responseœ,œidœ:œPrompt-u7GZRœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}", "selected": false, "source": "OpenAIModel-Rc3MO", "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-Rc3MOœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}", "target": "Prompt-u7GZR", "targetHandle": "{œfieldNameœ: œprevious_responseœ, œidœ: œPrompt-u7GZRœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "Prompt", "id": "Prompt-yDDjW", "name": "prompt", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "input_value", "id": "OpenAIModel-zhgF5", "inputTypes": [ "Message" ], "type": "str" } }, "id": "reactflow__edge-Prompt-yDDjW{œdataTypeœ:œPromptœ,œidœ:œPrompt-yDDjWœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-zhgF5{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-zhgF5œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}", "selected": false, "source": "Prompt-yDDjW", "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-yDDjWœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}", "target": "OpenAIModel-zhgF5", "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-zhgF5œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "ChatInput", "id": "ChatInput-Mzp4f", "name": "message", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "input_value", "id": "Prompt-yDDjW", "inputTypes": [ "Message", "Text" ], "type": "str" } }, "id": "reactflow__edge-ChatInput-Mzp4f{œdataTypeœ:œChatInputœ,œidœ:œChatInput-Mzp4fœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-yDDjW{œfieldNameœ:œinput_valueœ,œidœ:œPrompt-yDDjWœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}", "selected": false, "source": "ChatInput-Mzp4f", "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-Mzp4fœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}", "target": "Prompt-yDDjW", "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œPrompt-yDDjWœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "OpenAIModel", "id": "OpenAIModel-zhgF5", "name": "text_output", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "input_value", "id": "ChatOutput-mWv8X", "inputTypes": [ "Message" ], "type": "str" } }, "id": "reactflow__edge-OpenAIModel-zhgF5{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-zhgF5œ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-mWv8X{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-mWv8Xœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}", "selected": false, "source": "OpenAIModel-zhgF5", "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-zhgF5œ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}", "target": "ChatOutput-mWv8X", "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-mWv8Xœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "TavilyAISearch", "id": "TavilyAISearch-rI4aD", "name": "api_build_tool", "output_types": [ "Tool" ] }, "targetHandle": { "fieldName": "tools", "id": "Agent-9E8IU", "inputTypes": [ "Tool", "BaseTool", "StructuredTool" ], "type": "other" } }, "id": "reactflow__edge-TavilyAISearch-rI4aD{œdataTypeœ:œTavilyAISearchœ,œidœ:œTavilyAISearch-rI4aDœ,œnameœ:œapi_build_toolœ,œoutput_typesœ:[œToolœ]}-Agent-9E8IU{œfieldNameœ:œtoolsœ,œidœ:œAgent-9E8IUœ,œinputTypesœ:[œToolœ,œBaseToolœ,œStructuredToolœ],œtypeœ:œotherœ}", "selected": false, "source": "TavilyAISearch-rI4aD", "sourceHandle": "{œdataTypeœ: œTavilyAISearchœ, œidœ: œTavilyAISearch-rI4aDœ, œnameœ: œapi_build_toolœ, œoutput_typesœ: [œToolœ]}", "target": "Agent-9E8IU", "targetHandle": "{œfieldNameœ: œtoolsœ, œidœ: œAgent-9E8IUœ, œinputTypesœ: [œToolœ, œBaseToolœ, œStructuredToolœ], œtypeœ: œotherœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "Prompt", "id": "Prompt-u7GZR", "name": "prompt", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "input_value", "id": "Agent-9E8IU", "inputTypes": [ "Message" ], "type": "str" } }, "id": "reactflow__edge-Prompt-u7GZR{œdataTypeœ:œPromptœ,œidœ:œPrompt-u7GZRœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-Agent-9E8IU{œfieldNameœ:œinput_valueœ,œidœ:œAgent-9E8IUœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}", "selected": false, "source": "Prompt-u7GZR", "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-u7GZRœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}", "target": "Agent-9E8IU", "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œAgent-9E8IUœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "Agent", "id": "Agent-9E8IU", "name": "response", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "search_results", "id": "Prompt-yDDjW", "inputTypes": [ "Message", "Text" ], "type": "str" } }, "id": "reactflow__edge-Agent-9E8IU{œdataTypeœ:œAgentœ,œidœ:œAgent-9E8IUœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}-Prompt-yDDjW{œfieldNameœ:œsearch_resultsœ,œidœ:œPrompt-yDDjWœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}", "selected": false, "source": "Agent-9E8IU", "sourceHandle": "{œdataTypeœ: œAgentœ, œidœ: œAgent-9E8IUœ, œnameœ: œresponseœ, œoutput_typesœ: [œMessageœ]}", "target": "Prompt-yDDjW", "targetHandle": "{œfieldNameœ: œsearch_resultsœ, œidœ: œPrompt-yDDjWœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "Prompt", "id": "Prompt-T4lL6", "name": "prompt", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "system_message", "id": "OpenAIModel-Rc3MO", "inputTypes": [ "Message" ], "type": "str" } }, "id": "reactflow__edge-Prompt-T4lL6{œdataTypeœ:œPromptœ,œidœ:œPrompt-T4lL6œ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-Rc3MO{œfieldNameœ:œsystem_messageœ,œidœ:œOpenAIModel-Rc3MOœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}", "source": "Prompt-T4lL6", "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-T4lL6œ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}", "target": "OpenAIModel-Rc3MO", "targetHandle": "{œfieldNameœ: œsystem_messageœ, œidœ: œOpenAIModel-Rc3MOœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "ChatInput", "id": "ChatInput-Mzp4f", "name": "message", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "input_value", "id": "OpenAIModel-Rc3MO", "inputTypes": [ "Message" ], "type": "str" } }, "id": "reactflow__edge-ChatInput-Mzp4f{œdataTypeœ:œChatInputœ,œidœ:œChatInput-Mzp4fœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-Rc3MO{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-Rc3MOœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}", "source": "ChatInput-Mzp4f", "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-Mzp4fœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}", "target": "OpenAIModel-Rc3MO", "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-Rc3MOœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "Prompt", "id": "Prompt-f4xQ5", "name": "prompt", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "system_message", "id": "OpenAIModel-zhgF5", "inputTypes": [ "Message" ], "type": "str" } }, "id": "reactflow__edge-Prompt-f4xQ5{œdataTypeœ:œPromptœ,œidœ:œPrompt-f4xQ5œ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-zhgF5{œfieldNameœ:œsystem_messageœ,œidœ:œOpenAIModel-zhgF5œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}", "source": "Prompt-f4xQ5", "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-f4xQ5œ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}", "target": "OpenAIModel-zhgF5", "targetHandle": "{œfieldNameœ: œsystem_messageœ, œidœ: œOpenAIModel-zhgF5œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}" } ], "nodes": [ { "data": { "description": "Create a prompt template with dynamic variables.", "display_name": "Prompt", "id": "Prompt-u7GZR", "node": { "base_classes": [ "Message" ], "beta": false, "conditional_paths": [], "custom_fields": { "template": [ "previous_response" ] }, "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" }, "previous_response": { "advanced": false, "display_name": "previous_response", "dynamic": false, "field_type": "str", "fileTypes": [], "file_path": "", "info": "", "input_types": [ "Message", "Text" ], "list": false, "load_from_db": false, "multiline": true, "name": "previous_response", "placeholder": "", "required": false, "show": true, "title_case": false, "type": "str", "value": "" }, "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": "\n\nRESEARCH PLAN: {previous_response}\n\nUse Tavily Search to investigate the queries and analyze the findings.\nFocus on academic and reliable sources.\n\nSteps:\n1. Search using provided queries\n2. Analyze search results\n3. Verify source credibility\n4. Extract key findings\n\nFormat findings as:\n\nSEARCH RESULTS:\n[Key findings from searches]\n\nSOURCE ANALYSIS:\n[Credibility assessment]\n\nMAIN INSIGHTS:\n[Critical discoveries]\n\nEVIDENCE QUALITY:\n[Evaluation of findings]" }, "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": 347, "id": "Prompt-u7GZR", "position": { "x": 1803.2315476328304, "y": 839.0423490089254 }, "positionAbsolute": { "x": 1803.2315476328304, "y": 839.0423490089254 }, "selected": false, "type": "genericNode", "width": 320 }, { "data": { "id": "ChatInput-Mzp4f", "node": { "base_classes": [ "Message" ], "beta": false, "category": "inputs", "conditional_paths": [], "custom_fields": {}, "description": "Get chat inputs from the Playground.", "display_name": "Chat Input", "documentation": "", "edited": false, "field_order": [ "input_value", "should_store_message", "sender", "sender_name", "session_id", "files", "background_color", "chat_icon", "text_color" ], "frozen": false, "icon": "MessagesSquare", "key": "ChatInput", "legacy": false, "lf_version": "1.0.19.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, "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 DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\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\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Text\",\n value=\"\",\n info=\"Message to be passed as input.\",\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 ),\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={\"background_color\": background_color, \"text_color\": text_color, \"icon\": icon},\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": { "_input_type": "FileInput", "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, "title_case": false, "trace_as_metadata": true, "type": "file", "value": "" }, "input_value": { "_input_type": "MultilineInput", "advanced": false, "display_name": "Text", "dynamic": false, "info": "Message to be passed as input.", "input_types": [ "Message" ], "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": "Research the effectiveness of different prompt engineering techniques in controlling AI hallucinations, with focus on real-world applications and empirical studies." }, "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, "trace_as_metadata": true, "type": "str", "value": "User" }, "sender_name": { "_input_type": "MessageTextInput", "advanced": true, "display_name": "Sender Name", "dynamic": false, "info": "Name of the sender.", "input_types": [ "Message" ], "list": false, "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": { "_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, "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-Mzp4f", "position": { "x": 756.0075981758582, "y": 756.7423476254241 }, "positionAbsolute": { "x": 756.0075981758582, "y": 756.7423476254241 }, "selected": false, "type": "genericNode", "width": 320 }, { "data": { "description": "Display a chat message in the Playground.", "display_name": "Chat Output", "id": "ChatOutput-mWv8X", "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-mWv8X", "position": { "x": 3200.774558432761, "y": 853.9881404769172 }, "positionAbsolute": { "x": 3200.774558432761, "y": 853.9881404769172 }, "selected": false, "type": "genericNode", "width": 320 }, { "data": { "description": "Create a prompt template with dynamic variables.", "display_name": "Prompt", "id": "Prompt-yDDjW", "node": { "base_classes": [ "Message" ], "beta": false, "conditional_paths": [], "custom_fields": { "template": [ "search_results", "input_value" ] }, "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" }, "input_value": { "advanced": false, "display_name": "input_value", "dynamic": false, "field_type": "str", "fileTypes": [], "file_path": "", "info": "", "input_types": [ "Message", "Text" ], "list": false, "load_from_db": false, "multiline": true, "name": "input_value", "placeholder": "", "required": false, "show": true, "title_case": false, "type": "str", "value": "" }, "search_results": { "advanced": false, "display_name": "search_results", "dynamic": false, "field_type": "str", "fileTypes": [], "file_path": "", "info": "", "input_types": [ "Message", "Text" ], "list": false, "load_from_db": false, "multiline": true, "name": "search_results", "placeholder": "", "required": false, "show": true, "title_case": false, "type": "str", "value": "" }, "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": "RESEARCH FINDINGS: {search_results}\nORIGINAL QUERY: {input_value}\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": 433, "id": "Prompt-yDDjW", "position": { "x": 2504.138359606453, "y": 434.061360540584 }, "positionAbsolute": { "x": 2504.138359606453, "y": 434.061360540584 }, "selected": false, "type": "genericNode", "width": 320 }, { "data": { "description": "**Tavily AI** is a search engine optimized for LLMs and RAG, aimed at efficient, quick, and persistent search results. It can be used independently or as an agent tool.\n\nNote: Check 'Advanced' for all options.\n", "display_name": "Tavily AI Search", "id": "TavilyAISearch-rI4aD", "node": { "base_classes": [ "Data", "Tool" ], "beta": false, "conditional_paths": [], "custom_fields": {}, "description": "**Tavily AI** is a search engine optimized for LLMs and RAG, aimed at efficient, quick, and persistent search results. It can be used independently or as an agent tool.\n\nNote: Check 'Advanced' for all options.\n", "display_name": "Tavily AI Search", "documentation": "https://docs.tavily.com/", "edited": false, "field_order": [ "api_key", "query", "search_depth", "topic", "max_results", "include_images", "include_answer" ], "frozen": false, "icon": "TavilyIcon", "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": [ "api_key" ], "selected": "Data", "types": [ "Data" ], "value": "__UNDEFINED__" }, { "cache": true, "display_name": "Tool", "method": "build_tool", "name": "api_build_tool", "required_inputs": [ "api_key" ], "selected": "Tool", "types": [ "Tool" ], "value": "__UNDEFINED__" } ], "pinned": false, "template": { "_type": "Component", "api_key": { "_input_type": "SecretStrInput", "advanced": false, "display_name": "Tavily API Key", "dynamic": false, "info": "Your Tavily API Key.", "input_types": [ "Message" ], "load_from_db": true, "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 enum import Enum\n\nimport httpx\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 BoolInput, DropdownInput, IntInput, MessageTextInput, SecretStrInput\nfrom langflow.schema import Data\n\n\nclass TavilySearchDepth(Enum):\n BASIC = \"basic\"\n ADVANCED = \"advanced\"\n\n\nclass TavilySearchTopic(Enum):\n GENERAL = \"general\"\n NEWS = \"news\"\n\n\nclass TavilySearchSchema(BaseModel):\n query: str = Field(..., description=\"The search query you want to execute with Tavily.\")\n search_depth: TavilySearchDepth = Field(TavilySearchDepth.BASIC, description=\"The depth of the search.\")\n topic: TavilySearchTopic = Field(TavilySearchTopic.GENERAL, description=\"The category of the search.\")\n max_results: int = Field(5, description=\"The maximum number of search results to return.\")\n include_images: bool = Field(default=False, description=\"Include a list of query-related images in the response.\")\n include_answer: bool = Field(default=False, description=\"Include a short answer to original query.\")\n\n\nclass TavilySearchToolComponent(LCToolComponent):\n display_name = \"Tavily AI Search\"\n description = \"\"\"**Tavily AI** is a search engine optimized for LLMs and RAG, \\\n aimed at efficient, quick, and persistent search results. It can be used independently or as an agent tool.\n\nNote: Check 'Advanced' for all options.\n\"\"\"\n icon = \"TavilyIcon\"\n name = \"TavilyAISearch\"\n documentation = \"https://docs.tavily.com/\"\n\n inputs = [\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Tavily API Key\",\n required=True,\n info=\"Your Tavily API Key.\",\n ),\n MessageTextInput(\n name=\"query\",\n display_name=\"Search Query\",\n info=\"The search query you want to execute with Tavily.\",\n ),\n DropdownInput(\n name=\"search_depth\",\n display_name=\"Search Depth\",\n info=\"The depth of the search.\",\n options=list(TavilySearchDepth),\n value=TavilySearchDepth.ADVANCED,\n advanced=True,\n ),\n DropdownInput(\n name=\"topic\",\n display_name=\"Search Topic\",\n info=\"The category of the search.\",\n options=list(TavilySearchTopic),\n value=TavilySearchTopic.GENERAL,\n advanced=True,\n ),\n IntInput(\n name=\"max_results\",\n display_name=\"Max Results\",\n info=\"The maximum number of search results to return.\",\n value=5,\n advanced=True,\n ),\n BoolInput(\n name=\"include_images\",\n display_name=\"Include Images\",\n info=\"Include a list of query-related images in the response.\",\n value=True,\n advanced=True,\n ),\n BoolInput(\n name=\"include_answer\",\n display_name=\"Include Answer\",\n info=\"Include a short answer to original query.\",\n value=True,\n advanced=True,\n ),\n ]\n\n def run_model(self) -> list[Data]:\n # Convert string values to enum instances with validation\n try:\n search_depth_enum = (\n self.search_depth\n if isinstance(self.search_depth, TavilySearchDepth)\n else TavilySearchDepth(str(self.search_depth).lower())\n )\n except ValueError as e:\n error_message = f\"Invalid search depth value: {e!s}\"\n self.status = error_message\n return [Data(data={\"error\": error_message})]\n\n try:\n topic_enum = (\n self.topic if isinstance(self.topic, TavilySearchTopic) else TavilySearchTopic(str(self.topic).lower())\n )\n except ValueError as e:\n error_message = f\"Invalid topic value: {e!s}\"\n self.status = error_message\n return [Data(data={\"error\": error_message})]\n\n return self._tavily_search(\n self.query,\n search_depth=search_depth_enum,\n topic=topic_enum,\n max_results=self.max_results,\n include_images=self.include_images,\n include_answer=self.include_answer,\n )\n\n def build_tool(self) -> Tool:\n return StructuredTool.from_function(\n name=\"tavily_search\",\n description=\"Perform a web search using the Tavily API.\",\n func=self._tavily_search,\n args_schema=TavilySearchSchema,\n )\n\n def _tavily_search(\n self,\n query: str,\n *,\n search_depth: TavilySearchDepth = TavilySearchDepth.BASIC,\n topic: TavilySearchTopic = TavilySearchTopic.GENERAL,\n max_results: int = 5,\n include_images: bool = False,\n include_answer: bool = False,\n ) -> list[Data]:\n # Validate enum values\n if not isinstance(search_depth, TavilySearchDepth):\n msg = f\"Invalid search_depth value: {search_depth}\"\n raise TypeError(msg)\n if not isinstance(topic, TavilySearchTopic):\n msg = f\"Invalid topic value: {topic}\"\n raise TypeError(msg)\n\n try:\n url = \"https://api.tavily.com/search\"\n headers = {\n \"content-type\": \"application/json\",\n \"accept\": \"application/json\",\n }\n payload = {\n \"api_key\": self.api_key,\n \"query\": query,\n \"search_depth\": search_depth.value,\n \"topic\": topic.value,\n \"max_results\": max_results,\n \"include_images\": include_images,\n \"include_answer\": include_answer,\n }\n\n with httpx.Client() as client:\n response = client.post(url, json=payload, headers=headers)\n\n response.raise_for_status()\n search_results = response.json()\n\n data_results = [\n Data(\n data={\n \"title\": result.get(\"title\"),\n \"url\": result.get(\"url\"),\n \"content\": result.get(\"content\"),\n \"score\": result.get(\"score\"),\n }\n )\n for result in search_results.get(\"results\", [])\n ]\n\n if include_answer and search_results.get(\"answer\"):\n data_results.insert(0, Data(data={\"answer\": search_results[\"answer\"]}))\n\n if include_images and search_results.get(\"images\"):\n data_results.append(Data(data={\"images\": search_results[\"images\"]}))\n\n self.status = data_results # type: ignore[assignment]\n\n except httpx.HTTPStatusError as e:\n error_message = f\"HTTP error: {e.response.status_code} - {e.response.text}\"\n logger.debug(error_message)\n self.status = error_message\n raise ToolException(error_message) from e\n except Exception as e:\n error_message = f\"Unexpected error: {e}\"\n logger.opt(exception=True).debug(\"Error running Tavily Search\")\n self.status = error_message\n raise ToolException(error_message) from e\n return data_results\n" }, "include_answer": { "_input_type": "BoolInput", "advanced": true, "display_name": "Include Answer", "dynamic": false, "info": "Include a short answer to original query.", "list": false, "name": "include_answer", "placeholder": "", "required": false, "show": true, "title_case": false, "trace_as_metadata": true, "type": "bool", "value": true }, "include_images": { "_input_type": "BoolInput", "advanced": true, "display_name": "Include Images", "dynamic": false, "info": "Include a list of query-related images in the response.", "list": false, "name": "include_images", "placeholder": "", "required": false, "show": true, "title_case": false, "trace_as_metadata": true, "type": "bool", "value": true }, "max_results": { "_input_type": "IntInput", "advanced": true, "display_name": "Max Results", "dynamic": false, "info": "The maximum number of search results to return.", "list": false, "name": "max_results", "placeholder": "", "required": false, "show": true, "title_case": false, "trace_as_metadata": true, "type": "int", "value": 5 }, "query": { "_input_type": "MessageTextInput", "advanced": false, "display_name": "Search Query", "dynamic": false, "info": "The search query you want to execute with Tavily.", "input_types": [ "Message" ], "list": false, "load_from_db": false, "name": "query", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "type": "str", "value": "" }, "search_depth": { "_input_type": "DropdownInput", "advanced": true, "combobox": false, "display_name": "Search Depth", "dynamic": false, "info": "The depth of the search.", "load_from_db": false, "name": "search_depth", "options": [ "basic", "advanced" ], "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "type": "str", "value": "advanced" }, "topic": { "_input_type": "DropdownInput", "advanced": true, "combobox": false, "display_name": "Search Topic", "dynamic": false, "info": "The category of the search.", "load_from_db": false, "name": "topic", "options": [ "general", "news" ], "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "type": "str", "value": "general" } }, "tool_mode": false }, "type": "TavilyAISearch" }, "dragging": false, "height": 481, "id": "TavilyAISearch-rI4aD", "position": { "x": 1802.2291194402355, "y": 381.88177151343945 }, "positionAbsolute": { "x": 1802.2291194402355, "y": 381.88177151343945 }, "selected": false, "type": "genericNode", "width": 320 }, { "data": { "description": "Generates text using OpenAI LLMs.", "display_name": "OpenAI", "id": "OpenAIModel-Rc3MO", "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", "output_schema", "model_name", "openai_api_base", "api_key", "temperature", "seed", "output_parser" ], "frozen": false, "icon": "OpenAI", "legacy": false, "lf_version": "1.0.19.post2", "metadata": {}, "output_types": [], "outputs": [ { "cache": true, "display_name": "Text", "method": "text_response", "name": "text_output", "required_inputs": [], "selected": "Message", "types": [ "Message" ], "value": "__UNDEFINED__" }, { "cache": true, "display_name": "Language Model", "method": "build_model", "name": "model_output", "required_inputs": [], "selected": "LanguageModel", "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": [ "Message" ], "load_from_db": true, "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": "import operator\nfrom functools import reduce\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 OPENAI_MODEL_NAMES\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.inputs.inputs import HandleInput\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 DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\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 ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\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 HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n else:\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" }, "input_value": { "_input_type": "MessageInput", "advanced": false, "display_name": "Input", "dynamic": false, "info": "", "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": "" }, "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_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": "" }, "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" }, "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": "" }, "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 }, "stream": { "_input_type": "BoolInput", "advanced": false, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", "list": false, "name": "stream", "placeholder": "", "required": false, "show": true, "title_case": false, "trace_as_metadata": true, "type": "bool", "value": false }, "system_message": { "_input_type": "MessageTextInput", "advanced": false, "display_name": "System Message", "dynamic": false, "info": "System message to pass to the model.", "input_types": [ "Message" ], "list": false, "load_from_db": false, "name": "system_message", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "type": "str", "value": "" }, "temperature": { "_input_type": "FloatInput", "advanced": false, "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 } }, "tool_mode": false }, "type": "OpenAIModel" }, "dragging": false, "height": 630, "id": "OpenAIModel-Rc3MO", "position": { "x": 1457.8987895868838, "y": 543.8838473503562 }, "positionAbsolute": { "x": 1457.8987895868838, "y": 543.8838473503562 }, "selected": false, "type": "genericNode", "width": 320 }, { "data": { "description": "Generates text using OpenAI LLMs.", "display_name": "OpenAI", "id": "OpenAIModel-zhgF5", "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", "output_schema", "model_name", "openai_api_base", "api_key", "temperature", "seed", "output_parser" ], "frozen": false, "icon": "OpenAI", "legacy": false, "lf_version": "1.0.19.post2", "metadata": {}, "output_types": [], "outputs": [ { "cache": true, "display_name": "Text", "method": "text_response", "name": "text_output", "required_inputs": [], "selected": "Message", "types": [ "Message" ], "value": "__UNDEFINED__" }, { "cache": true, "display_name": "Language Model", "method": "build_model", "name": "model_output", "required_inputs": [], "selected": "LanguageModel", "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": [ "Message" ], "load_from_db": true, "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": "import operator\nfrom functools import reduce\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 OPENAI_MODEL_NAMES\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.inputs.inputs import HandleInput\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 DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\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 ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\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 HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n else:\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" }, "input_value": { "_input_type": "MessageInput", "advanced": false, "display_name": "Input", "dynamic": false, "info": "", "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": "" }, "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_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": "" }, "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" }, "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": "" }, "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 }, "stream": { "_input_type": "BoolInput", "advanced": false, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", "list": false, "name": "stream", "placeholder": "", "required": false, "show": true, "title_case": false, "trace_as_metadata": true, "type": "bool", "value": false }, "system_message": { "_input_type": "MessageTextInput", "advanced": false, "display_name": "System Message", "dynamic": false, "info": "System message to pass to the model.", "input_types": [ "Message" ], "list": false, "load_from_db": false, "name": "system_message", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "type": "str", "value": "" }, "temperature": { "_input_type": "FloatInput", "advanced": false, "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 } }, "tool_mode": false }, "type": "OpenAIModel" }, "dragging": false, "height": 630, "id": "OpenAIModel-zhgF5", "position": { "x": 2860.2941186979524, "y": 561.8661152181708 }, "positionAbsolute": { "x": 2860.2941186979524, "y": 561.8661152181708 }, "selected": false, "type": "genericNode", "width": 320 }, { "data": { "id": "note-gBUJ9", "node": { "description": "# Research Agent \n\nWelcome to the Research Agent! This flow helps you conduct in-depth research on various topics using AI-powered tools and analysis.\n\n## Instructions\n1. Enter Your Research Query\n - Type your research question or topic into the Chat Input node.\n - Be specific and clear about what you want to investigate.\n\n2. Generate Research Plan\n - The system will create a focused research plan based on your query.\n - This plan includes key search queries and priorities.\n\n3. Conduct Web Search\n - The Tavily AI Search tool will perform web searches using the generated queries.\n - It focuses on finding academic and reliable sources.\n\n4. Analyze and Synthesize\n - The AI agent will review the search results and create a comprehensive synthesis.\n - The report includes an executive summary, methodology, findings, and conclusions.\n\n5. Review the Output\n - Read the final report in the Chat Output node.\n - Use this information as a starting point for further research or decision-making.\n\nRemember: You can refine your initial query for more specific results! 🔍📊", "display_name": "", "documentation": "", "template": { "backgroundColor": "emerald" } }, "type": "note" }, "dragging": false, "height": 765, "id": "note-gBUJ9", "position": { "x": 471.4335708918645, "y": -9.732869247334605 }, "positionAbsolute": { "x": 471.4335708918645, "y": -9.732869247334605 }, "resizing": false, "selected": false, "style": { "height": 765, "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-9E8IU", "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": "You are a research analyst with access to Tavily Search." }, "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": 658, "id": "Agent-9E8IU", "position": { "x": 2156.60686936856, "y": 439.4579572266066 }, "positionAbsolute": { "x": 2156.60686936856, "y": 439.4579572266066 }, "selected": true, "type": "genericNode", "width": 320 }, { "data": { "description": "Create a prompt template with dynamic variables.", "display_name": "Prompt", "id": "Prompt-T4lL6", "node": { "base_classes": [ "Message" ], "beta": false, "conditional_paths": [], "custom_fields": { "template": [] }, "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" }, "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 an expert research assistant.\n\nCreate a focused research plan that will guide our search.\n\nFormat your response exactly as:\n\nRESEARCH OBJECTIVE:\n[Clear statement of research goal]\n\nKEY SEARCH QUERIES:\n1. [Primary academic search query]\n2. [Secondary search query]\n3. [Alternative search approach]\n\nSEARCH PRIORITIES:\n- [What types of sources to focus on]\n- [Key aspects to investigate]\n- [Specific areas to explore]" }, "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-T4lL6", "position": { "x": 1102.6079408836365, "y": 550.2148817052229 }, "positionAbsolute": { "x": 1102.6079408836365, "y": 550.2148817052229 }, "selected": false, "type": "genericNode", "width": 320 }, { "data": { "description": "Create a prompt template with dynamic variables.", "display_name": "Prompt", "id": "Prompt-f4xQ5", "node": { "base_classes": [ "Message" ], "beta": false, "conditional_paths": [], "custom_fields": { "template": [] }, "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" }, "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 research synthesis expert.\n\nCreate a comprehensive synthesis and report of our findings.\n\nFormat your response as:\n\nEXECUTIVE SUMMARY:\n[Key findings and implications]\n\nMETHODOLOGY:\n- Search Strategy Used\n- Sources Analyzed\n- Quality Assessment\n\nFINDINGS & ANALYSIS:\n[Detailed discussion of discoveries]\n\nCONCLUSIONS:\n[Main takeaways and insights]\n\nFUTURE DIRECTIONS:\n[Suggested next steps]\n\nIMPORTANT: For each major point or finding, include the relevant source link in square brackets at the end of the sentence or paragraph. For example: \"Harvard has developed a solid-state battery that charges in minutes. [Source: https://example.com/article]\"\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-f4xQ5", "position": { "x": 2498.9482347755306, "y": 889.7491088138673 }, "positionAbsolute": { "x": 2498.9482347755306, "y": 889.7491088138673 }, "selected": false, "type": "genericNode", "width": 320 }, { "data": { "id": "note-86Le6", "node": { "description": "# 🔑 Tavily AI Search Needs API Key\n\nYou can get 1000 searches/month free [here](https://tavily.com/) ", "display_name": "", "documentation": "", "template": { "backgroundColor": "lime" } }, "type": "note" }, "dragging": false, "height": 325, "id": "note-86Le6", "position": { "x": 1797.5781951055678, "y": 206.30509875543274 }, "positionAbsolute": { "x": 1797.5781951055678, "y": 206.30509875543274 }, "selected": false, "type": "noteNode", "width": 325 } ], "viewport": { "x": -1627.3021649072248, "y": -234.56097308583958, "zoom": 0.9538000505518524 } }, "description": "starterProjects.researchAgent.description", "endpoint_name": null, "gradient": "5", "icon": "TextSearchIcon", "id": "67b16861-1344-465b-963a-c1c338623438", "is_component": false, "last_tested_version": "1.0.19.post2", "name": "starterProjects.researchAgent.name", "tags": [ "assistants", "agents" ] }