{ "data": { "edges": [ { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "ChatInput", "id": "ChatInput-iYW45", "name": "message", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "input_value", "id": "Prompt-aHVYv", "inputTypes": [ "Message", "Text" ], "type": "str" } }, "id": "reactflow__edge-ChatInput-iYW45{œdataTypeœ:œChatInputœ,œidœ:œChatInput-iYW45œ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-aHVYv{œfieldNameœ:œinput_valueœ,œidœ:œPrompt-aHVYvœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}", "selected": false, "source": "ChatInput-iYW45", "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-iYW45œ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}", "target": "Prompt-aHVYv", "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œPrompt-aHVYvœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "LanguageModelComponent", "id": "LanguageModelComponent-TZiUW", "name": "text_output", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "previous_response", "id": "Prompt-rQ5Up", "inputTypes": [ "Message", "Text" ], "type": "str" } }, "id": "reactflow__edge-LanguageModelComponent-TZiUW{œdataTypeœ:œLanguageModelComponentœ,œidœ:œLanguageModelComponent-TZiUWœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-Prompt-rQ5Up{œfieldNameœ:œprevious_responseœ,œidœ:œPrompt-rQ5Upœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}", "selected": false, "source": "LanguageModelComponent-TZiUW", "sourceHandle": "{œdataTypeœ: œLanguageModelComponentœ, œidœ: œLanguageModelComponent-TZiUWœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}", "target": "Prompt-rQ5Up", "targetHandle": "{œfieldNameœ: œprevious_responseœ, œidœ: œPrompt-rQ5Upœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "Prompt", "id": "Prompt-F8cZX", "name": "prompt", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "system_message", "id": "LanguageModelComponent-TZiUW", "inputTypes": [ "Message" ], "type": "str" } }, "id": "reactflow__edge-Prompt-F8cZX{œdataTypeœ:œPromptœ,œidœ:œPrompt-F8cZXœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-LanguageModelComponent-TZiUW{œfieldNameœ:œsystem_messageœ,œidœ:œLanguageModelComponent-TZiUWœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}", "selected": false, "source": "Prompt-F8cZX", "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-F8cZXœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}", "target": "LanguageModelComponent-TZiUW", "targetHandle": "{œfieldNameœ: œsystem_messageœ, œidœ: œLanguageModelComponent-TZiUWœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "ChatInput", "id": "ChatInput-iYW45", "name": "message", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "input_value", "id": "LanguageModelComponent-TZiUW", "inputTypes": [ "Message" ], "type": "str" } }, "id": "reactflow__edge-ChatInput-iYW45{œdataTypeœ:œChatInputœ,œidœ:œChatInput-iYW45œ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-LanguageModelComponent-TZiUW{œfieldNameœ:œinput_valueœ,œidœ:œLanguageModelComponent-TZiUWœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}", "selected": false, "source": "ChatInput-iYW45", "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-iYW45œ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}", "target": "LanguageModelComponent-TZiUW", "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œLanguageModelComponent-TZiUWœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "Prompt", "id": "Prompt-TbFFl", "name": "prompt", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "system_message", "id": "LanguageModelComponent-80mt4", "inputTypes": [ "Message" ], "type": "str" } }, "id": "reactflow__edge-Prompt-TbFFl{œdataTypeœ:œPromptœ,œidœ:œPrompt-TbFFlœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-LanguageModelComponent-80mt4{œfieldNameœ:œsystem_messageœ,œidœ:œLanguageModelComponent-80mt4œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}", "selected": false, "source": "Prompt-TbFFl", "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-TbFFlœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}", "target": "LanguageModelComponent-80mt4", "targetHandle": "{œfieldNameœ: œsystem_messageœ, œidœ: œLanguageModelComponent-80mt4œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "Prompt", "id": "Prompt-aHVYv", "name": "prompt", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "input_value", "id": "LanguageModelComponent-80mt4", "inputTypes": [ "Message" ], "type": "str" } }, "id": "reactflow__edge-Prompt-aHVYv{œdataTypeœ:œPromptœ,œidœ:œPrompt-aHVYvœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-LanguageModelComponent-80mt4{œfieldNameœ:œinput_valueœ,œidœ:œLanguageModelComponent-80mt4œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}", "selected": false, "source": "Prompt-aHVYv", "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-aHVYvœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}", "target": "LanguageModelComponent-80mt4", "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œLanguageModelComponent-80mt4œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "LanguageModelComponent", "id": "LanguageModelComponent-80mt4", "name": "text_output", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "input_value", "id": "ChatOutput-gZuRk", "inputTypes": [ "Data", "JSON", "DataFrame", "Table", "Message" ], "type": "other" } }, "id": "reactflow__edge-LanguageModelComponent-80mt4{œdataTypeœ:œLanguageModelComponentœ,œidœ:œLanguageModelComponent-80mt4œ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-gZuRk{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-gZuRkœ,œinputTypesœ:[œDataœ,œJSONœ,œDataFrameœ,œTableœ,œMessageœ],œtypeœ:œotherœ}", "selected": false, "source": "LanguageModelComponent-80mt4", "sourceHandle": "{œdataTypeœ: œLanguageModelComponentœ, œidœ: œLanguageModelComponent-80mt4œ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}", "target": "ChatOutput-gZuRk", "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-gZuRkœ, œinputTypesœ: [œDataœ, œJSONœ, œDataFrameœ, œTableœ, œMessageœ], œtypeœ: œotherœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "Prompt", "id": "Prompt-rQ5Up", "name": "prompt", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "input_value", "id": "Agent-mIgZ5", "inputTypes": [ "Message" ], "type": "str" } }, "id": "reactflow__edge-Prompt-rQ5Up{œdataTypeœ:œPromptœ,œidœ:œPrompt-rQ5Upœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-Agent-mIgZ5{œfieldNameœ:œinput_valueœ,œidœ:œAgent-mIgZ5œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}", "selected": false, "source": "Prompt-rQ5Up", "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-rQ5Upœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}", "target": "Agent-mIgZ5", "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œAgent-mIgZ5œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "TavilySearchComponent", "id": "TavilySearchComponent-bJRoU", "name": "component_as_tool", "output_types": [ "Tool" ] }, "targetHandle": { "fieldName": "tools", "id": "Agent-mIgZ5", "inputTypes": [ "Tool" ], "type": "other" } }, "id": "reactflow__edge-TavilySearchComponent-bJRoU{œdataTypeœ:œTavilySearchComponentœ,œidœ:œTavilySearchComponent-bJRoUœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}-Agent-mIgZ5{œfieldNameœ:œtoolsœ,œidœ:œAgent-mIgZ5œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}", "selected": false, "source": "TavilySearchComponent-bJRoU", "sourceHandle": "{œdataTypeœ: œTavilySearchComponentœ, œidœ: œTavilySearchComponent-bJRoUœ, œnameœ: œcomponent_as_toolœ, œoutput_typesœ: [œToolœ]}", "target": "Agent-mIgZ5", "targetHandle": "{œfieldNameœ: œtoolsœ, œidœ: œAgent-mIgZ5œ, œinputTypesœ: [œToolœ], œtypeœ: œotherœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "Agent", "id": "Agent-mIgZ5", "name": "response", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "search_results", "id": "Prompt-aHVYv", "inputTypes": [ "Message", "Text" ], "type": "str" } }, "id": "reactflow__edge-Agent-mIgZ5{œdataTypeœ:œAgentœ,œidœ:œAgent-mIgZ5œ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}-Prompt-aHVYv{œfieldNameœ:œsearch_resultsœ,œidœ:œPrompt-aHVYvœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}", "selected": false, "source": "Agent-mIgZ5", "sourceHandle": "{œdataTypeœ: œAgentœ, œidœ: œAgent-mIgZ5œ, œnameœ: œresponseœ, œoutput_typesœ: [œMessageœ]}", "target": "Prompt-aHVYv", "targetHandle": "{œfieldNameœ: œsearch_resultsœ, œidœ: œPrompt-aHVYvœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}" } ], "nodes": [ { "data": { "description": "Create a prompt template with dynamic variables.", "display_name": "Prompt Template", "id": "Prompt-rQ5Up", "node": { "base_classes": [ "Message" ], "beta": false, "conditional_paths": [], "custom_fields": { "template": [ "previous_response" ] }, "description": "Create a prompt template with dynamic variables.", "display_name": "Prompt Template", "documentation": "", "edited": false, "field_order": [ "template", "use_double_brackets", "tool_placeholder" ], "frozen": false, "icon": "prompts", "legacy": false, "lf_version": "1.4.3", "metadata": { "code_hash": "3d3fd7b8a36f", "dependencies": { "dependencies": [ { "name": "lfx", "version": null } ], "total_dependencies": 1 }, "module": "lfx.components.models_and_agents.prompt.PromptComponent" }, "output_types": [], "outputs": [ { "allows_loop": false, "cache": true, "display_name": "Prompt", "group_outputs": false, "method": "build_prompt", "name": "prompt", "selected": "Message", "tool_mode": true, "types": [ "Message" ], "value": "__UNDEFINED__" } ], "pinned": false, "template": { "_type": "Component", "code": { "advanced": true, "dynamic": true, "fileTypes": [], "file_path": "", "info": "", "list": false, "load_from_db": false, "multiline": true, "name": "code", "password": false, "placeholder": "", "required": true, "show": true, "title_case": false, "type": "code", "value": "from typing import Any\n\nfrom lfx.base.prompts.api_utils import process_prompt_template\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.inputs.input_mixin import FieldTypes\nfrom lfx.inputs.inputs import DefaultPromptField\nfrom lfx.io import BoolInput, MessageTextInput, Output, PromptInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.template.utils import update_template_values\nfrom lfx.utils.mustache_security import validate_mustache_template\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt Template\"\n description: str = \"Create a prompt template with dynamic variables.\"\n documentation: str = \"https://docs.langflow.org/components-prompts\"\n icon = \"prompts\"\n trace_type = \"prompt\"\n name = \"Prompt Template\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n BoolInput(\n name=\"use_double_brackets\",\n display_name=\"Use Double Brackets\",\n value=False,\n advanced=True,\n info=\"Use {{variable}} syntax instead of {variable}.\",\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"tool_placeholder\",\n display_name=\"Tool Placeholder\",\n tool_mode=True,\n advanced=True,\n show=False,\n info=\"A placeholder input for tool mode.\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n ]\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n \"\"\"Update the template field type based on the selected mode.\"\"\"\n if field_name == \"use_double_brackets\":\n # Change the template field type based on mode\n is_mustache = field_value is True\n if is_mustache:\n build_config[\"template\"][\"type\"] = FieldTypes.MUSTACHE_PROMPT.value\n else:\n build_config[\"template\"][\"type\"] = FieldTypes.PROMPT.value\n\n # Re-process the template to update variables when mode changes\n template_value = build_config.get(\"template\", {}).get(\"value\", \"\")\n if template_value:\n # Ensure custom_fields is properly initialized\n if \"custom_fields\" not in build_config:\n build_config[\"custom_fields\"] = {}\n\n # Clean up fields from the OLD mode before processing with NEW mode\n # This ensures we don't keep fields with wrong syntax even if validation fails\n old_custom_fields = build_config[\"custom_fields\"].get(\"template\", [])\n for old_field in list(old_custom_fields):\n # Remove the field from custom_fields and template\n if old_field in old_custom_fields:\n old_custom_fields.remove(old_field)\n build_config.pop(old_field, None)\n\n # Try to process template with new mode to add new variables\n # If validation fails, at least we cleaned up old fields\n try:\n # Validate mustache templates for security\n if is_mustache:\n validate_mustache_template(template_value)\n\n # Re-process template with new mode to add new variables\n _ = process_prompt_template(\n template=template_value,\n name=\"template\",\n custom_fields=build_config[\"custom_fields\"],\n frontend_node_template=build_config,\n is_mustache=is_mustache,\n )\n except ValueError as e:\n # If validation fails, we still updated the mode and cleaned old fields\n # User will see error when they try to save\n logger.debug(f\"Template validation failed during mode switch: {e}\")\n return build_config\n\n async def build_prompt(self) -> Message:\n use_double_brackets = self.use_double_brackets if hasattr(self, \"use_double_brackets\") else False\n template_format = \"mustache\" if use_double_brackets else \"f-string\"\n prompt = await Message.from_template_and_variables(template_format=template_format, **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 use_double_brackets = frontend_node[\"template\"].get(\"use_double_brackets\", {}).get(\"value\", False)\n is_mustache = use_double_brackets is True\n\n try:\n # Validate mustache templates for security\n if is_mustache:\n validate_mustache_template(prompt_template)\n\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 is_mustache=is_mustache,\n )\n except ValueError as e:\n # If validation fails, don't add variables but allow component to be created\n logger.debug(f\"Template validation failed in _update_template: {e}\")\n return frontend_node\n\n async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):\n \"\"\"This function is called after the code validation is done.\"\"\"\n frontend_node = await super().update_frontend_node(new_frontend_node, current_frontend_node)\n template = frontend_node[\"template\"][\"template\"][\"value\"]\n use_double_brackets = frontend_node[\"template\"].get(\"use_double_brackets\", {}).get(\"value\", False)\n is_mustache = use_double_brackets is True\n\n try:\n # Validate mustache templates for security\n if is_mustache:\n validate_mustache_template(template)\n\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 is_mustache=is_mustache,\n )\n except ValueError as e:\n # If validation fails, don't add variables but allow component to be updated\n logger.debug(f\"Template validation failed in update_frontend_node: {e}\")\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": "" }, "use_double_brackets": { "_input_type": "BoolInput", "advanced": true, "display_name": "Use Double Brackets", "dynamic": false, "info": "Use {{variable}} syntax instead of {variable}.", "list": false, "list_add_label": "Add More", "name": "use_double_brackets", "override_skip": false, "placeholder": "", "real_time_refresh": true, "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": true, "type": "bool", "value": false } }, "tool_mode": false }, "selected_output": "prompt", "type": "Prompt" }, "dragging": false, "id": "Prompt-rQ5Up", "measured": { "height": 347, "width": 320 }, "position": { "x": 1818.6564755787988, "y": 857.0380982792217 }, "positionAbsolute": { "x": 1803.2315476328304, "y": 839.0423490089254 }, "selected": false, "type": "genericNode" }, { "data": { "id": "ChatInput-iYW45", "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", "context_id", "files" ], "frozen": false, "icon": "MessagesSquare", "key": "ChatInput", "legacy": false, "lf_version": "1.4.3", "metadata": { "code_hash": "7a26c54d89ed", "dependencies": { "dependencies": [ { "name": "lfx", "version": null } ], "total_dependencies": 1 }, "module": "lfx.components.input_output.chat.ChatInput" }, "output_types": [], "outputs": [ { "allows_loop": false, "cache": true, "display_name": "Chat Message", "group_outputs": false, "method": "message_response", "name": "message", "selected": "Message", "tool_mode": true, "types": [ "Message" ], "value": "__UNDEFINED__" } ], "pinned": false, "template": { "_type": "Component", "code": { "advanced": true, "dynamic": true, "fileTypes": [], "file_path": "", "info": "", "list": false, "load_from_db": false, "multiline": true, "name": "code", "password": false, "placeholder": "", "required": true, "show": true, "title_case": false, "type": "code", "value": "from lfx.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.inputs.inputs import BoolInput\nfrom lfx.io import (\n DropdownInput,\n FileInput,\n MessageTextInput,\n MultilineInput,\n Output,\n)\nfrom lfx.schema.message import Message\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_USER,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n documentation: str = \"https://docs.langflow.org/chat-input-and-output\"\n icon = \"MessagesSquare\"\n name = \"ChatInput\"\n minimized = True\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Input Text\",\n value=\"\",\n info=\"Message to be passed as input.\",\n input_types=[],\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_USER,\n info=\"Type of sender.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_USER,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n FileInput(\n name=\"files\",\n display_name=\"Files\",\n file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n info=\"Files to be sent with the message.\",\n advanced=True,\n is_list=True,\n temp_file=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Chat Message\", name=\"message\", method=\"message_response\"),\n ]\n\n async def message_response(self) -> Message:\n # Ensure files is a list and filter out empty/None values\n files = self.files if self.files else []\n if files and not isinstance(files, list):\n files = [files]\n # Filter out None/empty values\n files = [f for f in files if f is not None and f != \"\"]\n\n session_id = self.session_id or self.graph.session_id or \"\"\n message = await Message.create(\n text=self.input_value,\n sender=self.sender,\n sender_name=self.sender_name,\n session_id=session_id,\n context_id=self.context_id,\n files=files,\n )\n if session_id and isinstance(message, Message) and self.should_store_message:\n stored_message = await self.send_message(\n message,\n )\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n" }, "context_id": { "_input_type": "MessageTextInput", "advanced": true, "display_name": "Context ID", "dynamic": false, "info": "The context ID of the chat. Adds an extra layer to the local memory.", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": false, "name": "context_id", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "type": "str", "value": "" }, "files": { "_input_type": "FileInput", "advanced": true, "display_name": "Files", "dynamic": false, "fileTypes": [ "csv", "json", "pdf", "txt", "md", "mdx", "yaml", "yml", "xml", "html", "htm", "docx", "py", "sh", "sql", "js", "ts", "tsx", "jpg", "jpeg", "png", "bmp", "image" ], "file_path": "", "info": "Files to be sent with the message.", "list": true, "name": "files", "placeholder": "", "required": false, "show": true, "temp_file": true, "title_case": false, "trace_as_metadata": true, "type": "file", "value": "" }, "input_value": { "_input_type": "MultilineInput", "advanced": false, "display_name": "Input Text", "dynamic": false, "info": "Message to be passed as input.", "input_types": [], "list": false, "load_from_db": false, "multiline": true, "name": "input_value", "placeholder": "", "required": false, "show": true, "title_case": false, "trace_as_input": true, "trace_as_metadata": true, "type": "str", "value": "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 } } }, "selected_output": "message", "type": "ChatInput" }, "dragging": false, "id": "ChatInput-iYW45", "measured": { "height": 234, "width": 320 }, "position": { "x": 698.6950441526648, "y": 807.1773951658345 }, "positionAbsolute": { "x": 756.0075981758582, "y": 756.7423476254241 }, "selected": false, "type": "genericNode" }, { "data": { "description": "Create a prompt template with dynamic variables.", "display_name": "Prompt Template", "id": "Prompt-aHVYv", "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 Template", "documentation": "", "edited": false, "field_order": [ "template", "use_double_brackets", "tool_placeholder" ], "frozen": false, "icon": "prompts", "legacy": false, "lf_version": "1.4.3", "metadata": { "code_hash": "3d3fd7b8a36f", "dependencies": { "dependencies": [ { "name": "lfx", "version": null } ], "total_dependencies": 1 }, "module": "lfx.components.models_and_agents.prompt.PromptComponent" }, "output_types": [], "outputs": [ { "allows_loop": false, "cache": true, "display_name": "Prompt", "group_outputs": false, "method": "build_prompt", "name": "prompt", "selected": "Message", "tool_mode": true, "types": [ "Message" ], "value": "__UNDEFINED__" } ], "pinned": false, "template": { "_type": "Component", "code": { "advanced": true, "dynamic": true, "fileTypes": [], "file_path": "", "info": "", "list": false, "load_from_db": false, "multiline": true, "name": "code", "password": false, "placeholder": "", "required": true, "show": true, "title_case": false, "type": "code", "value": "from typing import Any\n\nfrom lfx.base.prompts.api_utils import process_prompt_template\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.inputs.input_mixin import FieldTypes\nfrom lfx.inputs.inputs import DefaultPromptField\nfrom lfx.io import BoolInput, MessageTextInput, Output, PromptInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.template.utils import update_template_values\nfrom lfx.utils.mustache_security import validate_mustache_template\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt Template\"\n description: str = \"Create a prompt template with dynamic variables.\"\n documentation: str = \"https://docs.langflow.org/components-prompts\"\n icon = \"prompts\"\n trace_type = \"prompt\"\n name = \"Prompt Template\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n BoolInput(\n name=\"use_double_brackets\",\n display_name=\"Use Double Brackets\",\n value=False,\n advanced=True,\n info=\"Use {{variable}} syntax instead of {variable}.\",\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"tool_placeholder\",\n display_name=\"Tool Placeholder\",\n tool_mode=True,\n advanced=True,\n show=False,\n info=\"A placeholder input for tool mode.\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n ]\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n \"\"\"Update the template field type based on the selected mode.\"\"\"\n if field_name == \"use_double_brackets\":\n # Change the template field type based on mode\n is_mustache = field_value is True\n if is_mustache:\n build_config[\"template\"][\"type\"] = FieldTypes.MUSTACHE_PROMPT.value\n else:\n build_config[\"template\"][\"type\"] = FieldTypes.PROMPT.value\n\n # Re-process the template to update variables when mode changes\n template_value = build_config.get(\"template\", {}).get(\"value\", \"\")\n if template_value:\n # Ensure custom_fields is properly initialized\n if \"custom_fields\" not in build_config:\n build_config[\"custom_fields\"] = {}\n\n # Clean up fields from the OLD mode before processing with NEW mode\n # This ensures we don't keep fields with wrong syntax even if validation fails\n old_custom_fields = build_config[\"custom_fields\"].get(\"template\", [])\n for old_field in list(old_custom_fields):\n # Remove the field from custom_fields and template\n if old_field in old_custom_fields:\n old_custom_fields.remove(old_field)\n build_config.pop(old_field, None)\n\n # Try to process template with new mode to add new variables\n # If validation fails, at least we cleaned up old fields\n try:\n # Validate mustache templates for security\n if is_mustache:\n validate_mustache_template(template_value)\n\n # Re-process template with new mode to add new variables\n _ = process_prompt_template(\n template=template_value,\n name=\"template\",\n custom_fields=build_config[\"custom_fields\"],\n frontend_node_template=build_config,\n is_mustache=is_mustache,\n )\n except ValueError as e:\n # If validation fails, we still updated the mode and cleaned old fields\n # User will see error when they try to save\n logger.debug(f\"Template validation failed during mode switch: {e}\")\n return build_config\n\n async def build_prompt(self) -> Message:\n use_double_brackets = self.use_double_brackets if hasattr(self, \"use_double_brackets\") else False\n template_format = \"mustache\" if use_double_brackets else \"f-string\"\n prompt = await Message.from_template_and_variables(template_format=template_format, **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 use_double_brackets = frontend_node[\"template\"].get(\"use_double_brackets\", {}).get(\"value\", False)\n is_mustache = use_double_brackets is True\n\n try:\n # Validate mustache templates for security\n if is_mustache:\n validate_mustache_template(prompt_template)\n\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 is_mustache=is_mustache,\n )\n except ValueError as e:\n # If validation fails, don't add variables but allow component to be created\n logger.debug(f\"Template validation failed in _update_template: {e}\")\n return frontend_node\n\n async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):\n \"\"\"This function is called after the code validation is done.\"\"\"\n frontend_node = await super().update_frontend_node(new_frontend_node, current_frontend_node)\n template = frontend_node[\"template\"][\"template\"][\"value\"]\n use_double_brackets = frontend_node[\"template\"].get(\"use_double_brackets\", {}).get(\"value\", False)\n is_mustache = use_double_brackets is True\n\n try:\n # Validate mustache templates for security\n if is_mustache:\n validate_mustache_template(template)\n\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 is_mustache=is_mustache,\n )\n except ValueError as e:\n # If validation fails, don't add variables but allow component to be updated\n logger.debug(f\"Template validation failed in update_frontend_node: {e}\")\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": "" }, "use_double_brackets": { "_input_type": "BoolInput", "advanced": true, "display_name": "Use Double Brackets", "dynamic": false, "info": "Use {{variable}} syntax instead of {variable}.", "list": false, "list_add_label": "Add More", "name": "use_double_brackets", "override_skip": false, "placeholder": "", "real_time_refresh": true, "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": true, "type": "bool", "value": false } }, "tool_mode": false }, "selected_output": "prompt", "type": "Prompt" }, "dragging": false, "id": "Prompt-aHVYv", "measured": { "height": 433, "width": 320 }, "position": { "x": 2504.138359606453, "y": 434.061360540584 }, "positionAbsolute": { "x": 2504.138359606453, "y": 434.061360540584 }, "selected": false, "type": "genericNode" }, { "data": { "id": "note-RH5AH", "node": { "description": "# 📖 README\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## Quick start\n- Configure your **Model Provider** with your API credentials.\n- Add your **Tavily API Key** to the Tavily AI Search component.\n \n## Using the Flow\n - Type your research question or topic into the Chat Input node.\n - Be specific and clear about what you want to investigate.", "display_name": "", "documentation": "", "template": { "backgroundColor": "neutral" } }, "type": "note" }, "dragging": false, "height": 619, "id": "note-RH5AH", "measured": { "height": 619, "width": 353 }, "position": { "x": 695.7852857643643, "y": 75.02767400369514 }, "positionAbsolute": { "x": 471.4335708918645, "y": -9.732869247334605 }, "resizing": false, "selected": false, "style": { "height": 765, "width": 600 }, "type": "noteNode", "width": 353 }, { "data": { "description": "Create a prompt template with dynamic variables.", "display_name": "Prompt Template", "id": "Prompt-F8cZX", "node": { "base_classes": [ "Message" ], "beta": false, "conditional_paths": [], "custom_fields": { "template": [] }, "description": "Create a prompt template with dynamic variables.", "display_name": "Prompt Template", "documentation": "", "edited": false, "field_order": [ "template", "use_double_brackets", "tool_placeholder" ], "frozen": false, "icon": "prompts", "legacy": false, "lf_version": "1.4.3", "metadata": { "code_hash": "3d3fd7b8a36f", "dependencies": { "dependencies": [ { "name": "lfx", "version": null } ], "total_dependencies": 1 }, "module": "lfx.components.models_and_agents.prompt.PromptComponent" }, "output_types": [], "outputs": [ { "allows_loop": false, "cache": true, "display_name": "Prompt", "group_outputs": false, "method": "build_prompt", "name": "prompt", "selected": "Message", "tool_mode": true, "types": [ "Message" ], "value": "__UNDEFINED__" } ], "pinned": false, "template": { "_type": "Component", "code": { "advanced": true, "dynamic": true, "fileTypes": [], "file_path": "", "info": "", "list": false, "load_from_db": false, "multiline": true, "name": "code", "password": false, "placeholder": "", "required": true, "show": true, "title_case": false, "type": "code", "value": "from typing import Any\n\nfrom lfx.base.prompts.api_utils import process_prompt_template\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.inputs.input_mixin import FieldTypes\nfrom lfx.inputs.inputs import DefaultPromptField\nfrom lfx.io import BoolInput, MessageTextInput, Output, PromptInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.template.utils import update_template_values\nfrom lfx.utils.mustache_security import validate_mustache_template\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt Template\"\n description: str = \"Create a prompt template with dynamic variables.\"\n documentation: str = \"https://docs.langflow.org/components-prompts\"\n icon = \"prompts\"\n trace_type = \"prompt\"\n name = \"Prompt Template\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n BoolInput(\n name=\"use_double_brackets\",\n display_name=\"Use Double Brackets\",\n value=False,\n advanced=True,\n info=\"Use {{variable}} syntax instead of {variable}.\",\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"tool_placeholder\",\n display_name=\"Tool Placeholder\",\n tool_mode=True,\n advanced=True,\n show=False,\n info=\"A placeholder input for tool mode.\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n ]\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n \"\"\"Update the template field type based on the selected mode.\"\"\"\n if field_name == \"use_double_brackets\":\n # Change the template field type based on mode\n is_mustache = field_value is True\n if is_mustache:\n build_config[\"template\"][\"type\"] = FieldTypes.MUSTACHE_PROMPT.value\n else:\n build_config[\"template\"][\"type\"] = FieldTypes.PROMPT.value\n\n # Re-process the template to update variables when mode changes\n template_value = build_config.get(\"template\", {}).get(\"value\", \"\")\n if template_value:\n # Ensure custom_fields is properly initialized\n if \"custom_fields\" not in build_config:\n build_config[\"custom_fields\"] = {}\n\n # Clean up fields from the OLD mode before processing with NEW mode\n # This ensures we don't keep fields with wrong syntax even if validation fails\n old_custom_fields = build_config[\"custom_fields\"].get(\"template\", [])\n for old_field in list(old_custom_fields):\n # Remove the field from custom_fields and template\n if old_field in old_custom_fields:\n old_custom_fields.remove(old_field)\n build_config.pop(old_field, None)\n\n # Try to process template with new mode to add new variables\n # If validation fails, at least we cleaned up old fields\n try:\n # Validate mustache templates for security\n if is_mustache:\n validate_mustache_template(template_value)\n\n # Re-process template with new mode to add new variables\n _ = process_prompt_template(\n template=template_value,\n name=\"template\",\n custom_fields=build_config[\"custom_fields\"],\n frontend_node_template=build_config,\n is_mustache=is_mustache,\n )\n except ValueError as e:\n # If validation fails, we still updated the mode and cleaned old fields\n # User will see error when they try to save\n logger.debug(f\"Template validation failed during mode switch: {e}\")\n return build_config\n\n async def build_prompt(self) -> Message:\n use_double_brackets = self.use_double_brackets if hasattr(self, \"use_double_brackets\") else False\n template_format = \"mustache\" if use_double_brackets else \"f-string\"\n prompt = await Message.from_template_and_variables(template_format=template_format, **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 use_double_brackets = frontend_node[\"template\"].get(\"use_double_brackets\", {}).get(\"value\", False)\n is_mustache = use_double_brackets is True\n\n try:\n # Validate mustache templates for security\n if is_mustache:\n validate_mustache_template(prompt_template)\n\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 is_mustache=is_mustache,\n )\n except ValueError as e:\n # If validation fails, don't add variables but allow component to be created\n logger.debug(f\"Template validation failed in _update_template: {e}\")\n return frontend_node\n\n async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):\n \"\"\"This function is called after the code validation is done.\"\"\"\n frontend_node = await super().update_frontend_node(new_frontend_node, current_frontend_node)\n template = frontend_node[\"template\"][\"template\"][\"value\"]\n use_double_brackets = frontend_node[\"template\"].get(\"use_double_brackets\", {}).get(\"value\", False)\n is_mustache = use_double_brackets is True\n\n try:\n # Validate mustache templates for security\n if is_mustache:\n validate_mustache_template(template)\n\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 is_mustache=is_mustache,\n )\n except ValueError as e:\n # If validation fails, don't add variables but allow component to be updated\n logger.debug(f\"Template validation failed in update_frontend_node: {e}\")\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": "" }, "use_double_brackets": { "_input_type": "BoolInput", "advanced": true, "display_name": "Use Double Brackets", "dynamic": false, "info": "Use {{variable}} syntax instead of {variable}.", "list": false, "list_add_label": "Add More", "name": "use_double_brackets", "override_skip": false, "placeholder": "", "real_time_refresh": true, "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": true, "type": "bool", "value": false } }, "tool_mode": false }, "selected_output": "prompt", "type": "Prompt" }, "dragging": false, "id": "Prompt-F8cZX", "measured": { "height": 260, "width": 320 }, "position": { "x": 1102.6079408836365, "y": 550.2148817052229 }, "positionAbsolute": { "x": 1102.6079408836365, "y": 550.2148817052229 }, "selected": false, "type": "genericNode" }, { "data": { "description": "Create a prompt template with dynamic variables.", "display_name": "Prompt Template", "id": "Prompt-TbFFl", "node": { "base_classes": [ "Message" ], "beta": false, "conditional_paths": [], "custom_fields": { "template": [] }, "description": "Create a prompt template with dynamic variables.", "display_name": "Prompt Template", "documentation": "", "edited": false, "field_order": [ "template", "use_double_brackets", "tool_placeholder" ], "frozen": false, "icon": "prompts", "legacy": false, "lf_version": "1.4.3", "metadata": { "code_hash": "3d3fd7b8a36f", "dependencies": { "dependencies": [ { "name": "lfx", "version": null } ], "total_dependencies": 1 }, "module": "lfx.components.models_and_agents.prompt.PromptComponent" }, "output_types": [], "outputs": [ { "allows_loop": false, "cache": true, "display_name": "Prompt", "group_outputs": false, "method": "build_prompt", "name": "prompt", "selected": "Message", "tool_mode": true, "types": [ "Message" ], "value": "__UNDEFINED__" } ], "pinned": false, "template": { "_type": "Component", "code": { "advanced": true, "dynamic": true, "fileTypes": [], "file_path": "", "info": "", "list": false, "load_from_db": false, "multiline": true, "name": "code", "password": false, "placeholder": "", "required": true, "show": true, "title_case": false, "type": "code", "value": "from typing import Any\n\nfrom lfx.base.prompts.api_utils import process_prompt_template\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.inputs.input_mixin import FieldTypes\nfrom lfx.inputs.inputs import DefaultPromptField\nfrom lfx.io import BoolInput, MessageTextInput, Output, PromptInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.template.utils import update_template_values\nfrom lfx.utils.mustache_security import validate_mustache_template\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt Template\"\n description: str = \"Create a prompt template with dynamic variables.\"\n documentation: str = \"https://docs.langflow.org/components-prompts\"\n icon = \"prompts\"\n trace_type = \"prompt\"\n name = \"Prompt Template\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n BoolInput(\n name=\"use_double_brackets\",\n display_name=\"Use Double Brackets\",\n value=False,\n advanced=True,\n info=\"Use {{variable}} syntax instead of {variable}.\",\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"tool_placeholder\",\n display_name=\"Tool Placeholder\",\n tool_mode=True,\n advanced=True,\n show=False,\n info=\"A placeholder input for tool mode.\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n ]\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n \"\"\"Update the template field type based on the selected mode.\"\"\"\n if field_name == \"use_double_brackets\":\n # Change the template field type based on mode\n is_mustache = field_value is True\n if is_mustache:\n build_config[\"template\"][\"type\"] = FieldTypes.MUSTACHE_PROMPT.value\n else:\n build_config[\"template\"][\"type\"] = FieldTypes.PROMPT.value\n\n # Re-process the template to update variables when mode changes\n template_value = build_config.get(\"template\", {}).get(\"value\", \"\")\n if template_value:\n # Ensure custom_fields is properly initialized\n if \"custom_fields\" not in build_config:\n build_config[\"custom_fields\"] = {}\n\n # Clean up fields from the OLD mode before processing with NEW mode\n # This ensures we don't keep fields with wrong syntax even if validation fails\n old_custom_fields = build_config[\"custom_fields\"].get(\"template\", [])\n for old_field in list(old_custom_fields):\n # Remove the field from custom_fields and template\n if old_field in old_custom_fields:\n old_custom_fields.remove(old_field)\n build_config.pop(old_field, None)\n\n # Try to process template with new mode to add new variables\n # If validation fails, at least we cleaned up old fields\n try:\n # Validate mustache templates for security\n if is_mustache:\n validate_mustache_template(template_value)\n\n # Re-process template with new mode to add new variables\n _ = process_prompt_template(\n template=template_value,\n name=\"template\",\n custom_fields=build_config[\"custom_fields\"],\n frontend_node_template=build_config,\n is_mustache=is_mustache,\n )\n except ValueError as e:\n # If validation fails, we still updated the mode and cleaned old fields\n # User will see error when they try to save\n logger.debug(f\"Template validation failed during mode switch: {e}\")\n return build_config\n\n async def build_prompt(self) -> Message:\n use_double_brackets = self.use_double_brackets if hasattr(self, \"use_double_brackets\") else False\n template_format = \"mustache\" if use_double_brackets else \"f-string\"\n prompt = await Message.from_template_and_variables(template_format=template_format, **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 use_double_brackets = frontend_node[\"template\"].get(\"use_double_brackets\", {}).get(\"value\", False)\n is_mustache = use_double_brackets is True\n\n try:\n # Validate mustache templates for security\n if is_mustache:\n validate_mustache_template(prompt_template)\n\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 is_mustache=is_mustache,\n )\n except ValueError as e:\n # If validation fails, don't add variables but allow component to be created\n logger.debug(f\"Template validation failed in _update_template: {e}\")\n return frontend_node\n\n async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):\n \"\"\"This function is called after the code validation is done.\"\"\"\n frontend_node = await super().update_frontend_node(new_frontend_node, current_frontend_node)\n template = frontend_node[\"template\"][\"template\"][\"value\"]\n use_double_brackets = frontend_node[\"template\"].get(\"use_double_brackets\", {}).get(\"value\", False)\n is_mustache = use_double_brackets is True\n\n try:\n # Validate mustache templates for security\n if is_mustache:\n validate_mustache_template(template)\n\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 is_mustache=is_mustache,\n )\n except ValueError as e:\n # If validation fails, don't add variables but allow component to be updated\n logger.debug(f\"Template validation failed in update_frontend_node: {e}\")\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": "" }, "use_double_brackets": { "_input_type": "BoolInput", "advanced": true, "display_name": "Use Double Brackets", "dynamic": false, "info": "Use {{variable}} syntax instead of {variable}.", "list": false, "list_add_label": "Add More", "name": "use_double_brackets", "override_skip": false, "placeholder": "", "real_time_refresh": true, "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": true, "type": "bool", "value": false } }, "tool_mode": false }, "selected_output": "prompt", "type": "Prompt" }, "dragging": false, "id": "Prompt-TbFFl", "measured": { "height": 260, "width": 320 }, "position": { "x": 2498.9482347755306, "y": 889.7491088138673 }, "positionAbsolute": { "x": 2498.9482347755306, "y": 889.7491088138673 }, "selected": false, "type": "genericNode" }, { "data": { "id": "TavilySearchComponent-bJRoU", "node": { "base_classes": [ "Data", "JSON", "Message" ], "beta": false, "conditional_paths": [], "custom_fields": {}, "description": "**Tavily Search** is a search engine optimized for LLMs and RAG, aimed at efficient, quick, and persistent search results.", "display_name": "Tavily AI Search", "documentation": "", "edited": false, "field_order": [ "api_key", "query", "search_depth", "chunks_per_source", "topic", "days", "max_results", "include_answer", "time_range", "include_images", "include_domains", "exclude_domains", "include_raw_content" ], "frozen": false, "icon": "TavilyIcon", "legacy": false, "lf_version": "1.4.3", "metadata": { "code_hash": "5638a305a99c", "dependencies": { "dependencies": [ { "name": "httpx", "version": "0.28.1" }, { "name": "lfx", "version": null } ], "total_dependencies": 2 }, "module": "lfx.components.tavily.tavily_search.TavilySearchComponent" }, "minimized": false, "output_types": [], "outputs": [ { "allows_loop": false, "cache": true, "display_name": "Toolset", "group_outputs": false, "hidden": null, "method": "to_toolkit", "name": "component_as_tool", "options": null, "required_inputs": null, "selected": "Tool", "tool_mode": true, "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": [], "load_from_db": true, "name": "api_key", "password": true, "placeholder": "", "required": true, "show": true, "title_case": false, "type": "str", "value": "TAVILY_API_KEY" }, "chunks_per_source": { "_input_type": "IntInput", "advanced": true, "display_name": "Chunks Per Source", "dynamic": false, "info": "The number of content chunks to retrieve from each source (1-3). Only works with advanced search.", "list": false, "list_add_label": "Add More", "name": "chunks_per_source", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "type": "int", "value": 3 }, "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 httpx\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, IntInput, MessageTextInput, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.template.field.base import Output\n\n\nclass TavilySearchComponent(Component):\n display_name = \"Tavily Search API\"\n description = \"\"\"**Tavily Search** is a search engine optimized for LLMs and RAG, \\\n aimed at efficient, quick, and persistent search results.\"\"\"\n icon = \"TavilyIcon\"\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 tool_mode=True,\n ),\n DropdownInput(\n name=\"search_depth\",\n display_name=\"Search Depth\",\n info=\"The depth of the search.\",\n options=[\"basic\", \"advanced\"],\n value=\"advanced\",\n advanced=True,\n ),\n IntInput(\n name=\"chunks_per_source\",\n display_name=\"Chunks Per Source\",\n info=(\"The number of content chunks to retrieve from each source (1-3). Only works with advanced search.\"),\n value=3,\n advanced=True,\n ),\n DropdownInput(\n name=\"topic\",\n display_name=\"Search Topic\",\n info=\"The category of the search.\",\n options=[\"general\", \"news\"],\n value=\"general\",\n advanced=True,\n ),\n IntInput(\n name=\"days\",\n display_name=\"Days\",\n info=\"Number of days back from current date to include. Only available with news topic.\",\n value=7,\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_answer\",\n display_name=\"Include Answer\",\n info=\"Include a short answer to original query.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"time_range\",\n display_name=\"Time Range\",\n info=\"The time range back from the current date to filter results.\",\n options=[\"day\", \"week\", \"month\", \"year\"],\n value=None, # Default to None to make it optional\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 MessageTextInput(\n name=\"include_domains\",\n display_name=\"Include Domains\",\n info=\"Comma-separated list of domains to include in the search results.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"exclude_domains\",\n display_name=\"Exclude Domains\",\n info=\"Comma-separated list of domains to exclude from the search results.\",\n advanced=True,\n ),\n BoolInput(\n name=\"include_raw_content\",\n display_name=\"Include Raw Content\",\n info=\"Include the cleaned and parsed HTML content of each search result.\",\n value=False,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Table\", name=\"dataframe\", method=\"fetch_content_dataframe\"),\n ]\n\n def fetch_content(self) -> list[Data]:\n try:\n # Only process domains if they're provided\n include_domains = None\n exclude_domains = None\n\n if self.include_domains:\n include_domains = [domain.strip() for domain in self.include_domains.split(\",\") if domain.strip()]\n\n if self.exclude_domains:\n exclude_domains = [domain.strip() for domain in self.exclude_domains.split(\",\") if domain.strip()]\n\n url = \"https://api.tavily.com/search\"\n headers = {\n \"content-type\": \"application/json\",\n \"accept\": \"application/json\",\n }\n\n payload = {\n \"api_key\": self.api_key,\n \"query\": self.query,\n \"search_depth\": self.search_depth,\n \"topic\": self.topic,\n \"max_results\": self.max_results,\n \"include_images\": self.include_images,\n \"include_answer\": self.include_answer,\n \"include_raw_content\": self.include_raw_content,\n \"days\": self.days,\n \"time_range\": self.time_range,\n }\n\n # Only add domains to payload if they exist and have values\n if include_domains:\n payload[\"include_domains\"] = include_domains\n if exclude_domains:\n payload[\"exclude_domains\"] = exclude_domains\n\n # Add conditional parameters only if they should be included\n if self.search_depth == \"advanced\" and self.chunks_per_source:\n payload[\"chunks_per_source\"] = self.chunks_per_source\n\n if self.topic == \"news\" and self.days:\n payload[\"days\"] = int(self.days) # Ensure days is an integer\n\n # Add time_range if it's set\n if hasattr(self, \"time_range\") and self.time_range:\n payload[\"time_range\"] = self.time_range\n\n # Add timeout handling\n with httpx.Client(timeout=90.0) 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\n if self.include_answer and search_results.get(\"answer\"):\n data_results.append(Data(text=search_results[\"answer\"]))\n\n for result in search_results.get(\"results\", []):\n content = result.get(\"content\", \"\")\n result_data = {\n \"title\": result.get(\"title\"),\n \"url\": result.get(\"url\"),\n \"content\": content,\n \"score\": result.get(\"score\"),\n }\n if self.include_raw_content:\n result_data[\"raw_content\"] = result.get(\"raw_content\")\n\n data_results.append(Data(text=content, data=result_data))\n\n if self.include_images and search_results.get(\"images\"):\n data_results.append(Data(text=\"Images found\", data={\"images\": search_results[\"images\"]}))\n\n except httpx.TimeoutException:\n error_message = \"Request timed out (90s). Please try again or adjust parameters.\"\n logger.error(error_message)\n return [Data(text=error_message, data={\"error\": error_message})]\n except httpx.HTTPStatusError as exc:\n error_message = f\"HTTP error occurred: {exc.response.status_code} - {exc.response.text}\"\n logger.error(error_message)\n return [Data(text=error_message, data={\"error\": error_message})]\n except httpx.RequestError as exc:\n error_message = f\"Request error occurred: {exc}\"\n logger.error(error_message)\n return [Data(text=error_message, data={\"error\": error_message})]\n except ValueError as exc:\n error_message = f\"Invalid response format: {exc}\"\n logger.error(error_message)\n return [Data(text=error_message, data={\"error\": error_message})]\n else:\n self.status = data_results\n return data_results\n\n def fetch_content_dataframe(self) -> DataFrame:\n data = self.fetch_content()\n return DataFrame(data)\n" }, "days": { "_input_type": "IntInput", "advanced": true, "display_name": "Days", "dynamic": false, "info": "Number of days back from current date to include. Only available with news topic.", "list": false, "list_add_label": "Add More", "name": "days", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "type": "int", "value": 7 }, "exclude_domains": { "_input_type": "MessageTextInput", "advanced": true, "display_name": "Exclude Domains", "dynamic": false, "info": "Comma-separated list of domains to exclude from the search results.", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": false, "name": "exclude_domains", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "type": "str", "value": "" }, "include_answer": { "_input_type": "BoolInput", "advanced": true, "display_name": "Include Answer", "dynamic": false, "info": "Include a short answer to original query.", "list": false, "list_add_label": "Add More", "name": "include_answer", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "type": "bool", "value": true }, "include_domains": { "_input_type": "MessageTextInput", "advanced": true, "display_name": "Include Domains", "dynamic": false, "info": "Comma-separated list of domains to include in the search results.", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": false, "name": "include_domains", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "type": "str", "value": "" }, "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, "list_add_label": "Add More", "name": "include_images", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "type": "bool", "value": true }, "include_raw_content": { "_input_type": "BoolInput", "advanced": true, "display_name": "Include Raw Content", "dynamic": false, "info": "Include the cleaned and parsed HTML content of each search result.", "list": false, "list_add_label": "Add More", "name": "include_raw_content", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "type": "bool", "value": false }, "max_results": { "_input_type": "IntInput", "advanced": true, "display_name": "Max Results", "dynamic": false, "info": "The maximum number of search results to return.", "list": false, "list_add_label": "Add More", "name": "max_results", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": 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, "list_add_label": "Add More", "load_from_db": false, "name": "query", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": true, "trace_as_input": true, "trace_as_metadata": true, "type": "str", "value": "" }, "search_depth": { "_input_type": "DropdownInput", "advanced": true, "combobox": false, "dialog_inputs": {}, "display_name": "Search Depth", "dynamic": false, "info": "The depth of the search.", "name": "search_depth", "options": [ "basic", "advanced" ], "options_metadata": [], "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "type": "str", "value": "advanced" }, "time_range": { "_input_type": "DropdownInput", "advanced": true, "combobox": false, "dialog_inputs": {}, "display_name": "Time Range", "dynamic": false, "info": "The time range back from the current date to filter results.", "name": "time_range", "options": [ "day", "week", "month", "year" ], "options_metadata": [], "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "type": "str" }, "tools_metadata": { "_input_type": "ToolsInput", "advanced": false, "display_name": "Actions", "dynamic": false, "info": "Modify tool names and descriptions to help agents understand when to use each tool.", "is_list": true, "list_add_label": "Add More", "name": "tools_metadata", "placeholder": "", "real_time_refresh": true, "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "type": "tools", "value": [ { "args": { "query": { "default": "", "description": "The search query you want to execute with Tavily.", "title": "Query", "type": "string" } }, "description": "TavilySearchComponent. fetch_content_dataframe - **Tavily Search** is a search engine optimized for LLMs and RAG, aimed at efficient, quick, and persistent search results.", "display_description": "TavilySearchComponent. fetch_content_dataframe - **Tavily Search** is a search engine optimized for LLMs and RAG, aimed at efficient, quick, and persistent search results.", "display_name": "fetch_content_dataframe", "name": "fetch_content_dataframe", "readonly": false, "status": true, "tags": [ "fetch_content_dataframe" ] } ] }, "topic": { "_input_type": "DropdownInput", "advanced": true, "combobox": false, "dialog_inputs": {}, "display_name": "Search Topic", "dynamic": false, "info": "The category of the search.", "name": "topic", "options": [ "general", "news" ], "options_metadata": [], "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "type": "str", "value": "general" } }, "tool_mode": true }, "selected_output": "component_as_tool", "showNode": true, "type": "TavilySearchComponent" }, "dragging": false, "id": "TavilySearchComponent-bJRoU", "measured": { "height": 315, "width": 320 }, "position": { "x": 1802.928183797125, "y": 368.90338283211725 }, "selected": false, "type": "genericNode" }, { "data": { "id": "ChatOutput-gZuRk", "node": { "base_classes": [ "Message" ], "beta": false, "category": "outputs", "conditional_paths": [], "custom_fields": {}, "description": "Display a chat message in the Playground.", "display_name": "Chat Output", "documentation": "", "edited": false, "field_order": [ "input_value", "should_store_message", "sender", "sender_name", "session_id", "context_id", "data_template", "clean_data" ], "frozen": false, "icon": "MessagesSquare", "key": "ChatOutput", "legacy": false, "lf_version": "1.4.3", "metadata": { "code_hash": "84009527d08c", "dependencies": { "dependencies": [ { "name": "orjson", "version": "3.11.8" }, { "name": "fastapi", "version": "0.135.3" }, { "name": "lfx", "version": null } ], "total_dependencies": 3 }, "module": "lfx.components.input_output.chat_output.ChatOutput" }, "minimized": true, "output_types": [], "outputs": [ { "allows_loop": false, "cache": true, "display_name": "Output Message", "group_outputs": false, "method": "message_response", "name": "message", "selected": "Message", "tool_mode": true, "types": [ "Message" ], "value": "__UNDEFINED__" } ], "pinned": false, "score": 0.003169567463043492, "template": { "_type": "Component", "clean_data": { "_input_type": "BoolInput", "advanced": true, "display_name": "Basic Clean Data", "dynamic": false, "info": "Whether to clean data before converting to string.", "list": false, "list_add_label": "Add More", "name": "clean_data", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "type": "bool", "value": true }, "code": { "advanced": true, "dynamic": true, "fileTypes": [], "file_path": "", "info": "", "list": false, "load_from_db": false, "multiline": true, "name": "code", "password": false, "placeholder": "", "required": true, "show": true, "title_case": false, "type": "code", "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/chat-input-and-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"JSON\", \"DataFrame\", \"Table\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n BoolInput(\n name=\"clean_data\",\n display_name=\"Basic Clean Data\",\n value=True,\n advanced=True,\n info=\"Whether to clean data before converting to string.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, _, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message) and not self.is_connected_to_chat_input():\n message = self.input_value\n # Update message properties\n message.text = text\n # Preserve existing session_id from the incoming message if it exists\n existing_session_id = message.session_id\n else:\n message = Message(text=text)\n existing_session_id = None\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n # Preserve session_id from incoming message, or use component/graph session_id\n message.session_id = (\n self.session_id or existing_session_id or (self.graph.session_id if hasattr(self, \"graph\") else None) or \"\"\n )\n message.context_id = self.context_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if message.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n # Set accumulated token usage from all upstream LLM vertices.\n # This must happen AFTER send_message() because streaming captures\n # usage from chunks and would overwrite accumulated totals.\n if hasattr(self, \"_vertex\") and self._vertex is not None:\n accumulated_usage = self._vertex._accumulate_upstream_token_usage() # noqa: SLF001\n if accumulated_usage:\n message.properties.usage = accumulated_usage\n if self.should_store_message and message.get_id():\n message = await self._update_stored_message(message)\n await self._send_message_event(message, id_=message.get_id())\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n clean_data: bool = getattr(self, \"clean_data\", False)\n return \"\\n\".join([safe_convert(item, clean_data=clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" }, "context_id": { "_input_type": "MessageTextInput", "advanced": true, "display_name": "Context ID", "dynamic": false, "info": "The context ID of the chat. Adds an extra layer to the local memory.", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": false, "name": "context_id", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "type": "str", "value": "" }, "data_template": { "_input_type": "MessageTextInput", "advanced": true, "display_name": "Data Template", "dynamic": false, "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": false, "name": "data_template", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "type": "str", "value": "{text}" }, "input_value": { "_input_type": "HandleInput", "advanced": false, "display_name": "Inputs", "dynamic": false, "info": "Message to be passed as output.", "input_types": [ "Data", "JSON", "DataFrame", "Table", "Message" ], "list": false, "list_add_label": "Add More", "name": "input_value", "placeholder": "", "required": true, "show": true, "title_case": false, "trace_as_metadata": true, "type": "other", "value": "" }, "sender": { "_input_type": "DropdownInput", "advanced": true, "combobox": false, "dialog_inputs": {}, "display_name": "Sender Type", "dynamic": false, "info": "Type of sender.", "name": "sender", "options": [ "Machine", "User" ], "options_metadata": [], "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "type": "str", "value": "Machine" }, "sender_name": { "_input_type": "MessageTextInput", "advanced": true, "display_name": "Sender Name", "dynamic": false, "info": "Name of the sender.", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": false, "name": "sender_name", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "type": "str", "value": "AI" }, "session_id": { "_input_type": "MessageTextInput", "advanced": true, "display_name": "Session ID", "dynamic": false, "info": "The session ID of the chat. If empty, the current session ID parameter will be used.", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": false, "name": "session_id", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "type": "str", "value": "" }, "should_store_message": { "_input_type": "BoolInput", "advanced": true, "display_name": "Store Messages", "dynamic": false, "info": "Store the message in the history.", "list": false, "list_add_label": "Add More", "name": "should_store_message", "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "type": "bool", "value": true } }, "tool_mode": false }, "showNode": false, "type": "ChatOutput" }, "id": "ChatOutput-gZuRk", "measured": { "height": 48, "width": 192 }, "position": { "x": 3330, "y": 915 }, "selected": false, "type": "genericNode" }, { "data": { "id": "LanguageModelComponent-TZiUW", "node": { "base_classes": [ "LanguageModel", "Message" ], "beta": false, "conditional_paths": [], "custom_fields": {}, "description": "Runs a language model given a specified provider.", "display_name": "Language Model", "documentation": "https://docs.langflow.org/components-models", "edited": false, "field_order": [ "model", "api_key", "base_url_ibm_watsonx", "project_id", "ollama_base_url", "input_value", "system_message", "stream", "temperature", "max_tokens" ], "frozen": false, "icon": "brain-circuit", "last_updated": "2025-12-11T21:29:10.202Z", "legacy": false, "lf_version": "1.7.0", "metadata": { "code_hash": "7fe3257f2169", "dependencies": { "dependencies": [ { "name": "lfx", "version": null } ], "total_dependencies": 1 }, "keywords": [ "model", "llm", "language model", "large language model" ], "module": "lfx.components.models_and_agents.language_model.LanguageModelComponent" }, "minimized": false, "output_types": [], "outputs": [ { "allows_loop": false, "cache": true, "display_name": "Model Response", "group_outputs": false, "loop_types": null, "method": "text_response", "name": "text_output", "options": null, "required_inputs": null, "selected": "Message", "tool_mode": true, "types": [ "Message" ], "value": "__UNDEFINED__" }, { "allows_loop": false, "cache": true, "display_name": "Language Model", "group_outputs": false, "loop_types": null, "method": "build_model", "name": "model_output", "options": null, "required_inputs": null, "selected": "LanguageModel", "tool_mode": true, "types": [ "LanguageModel" ], "value": "__UNDEFINED__" } ], "pinned": false, "priority": 0, "template": { "_frontend_node_flow_id": { "value": "524f71dc-cc58-441a-a009-c71d6755765f" }, "_frontend_node_folder_id": { "value": "afaca9ee-97e3-4211-8569-722a4058ea5e" }, "_type": "Component", "api_key": { "_input_type": "SecretStrInput", "advanced": true, "display_name": "API Key", "dynamic": false, "info": "Model Provider API key", "input_types": [], "load_from_db": false, "name": "api_key", "override_skip": false, "password": true, "placeholder": "", "real_time_refresh": true, "required": false, "show": true, "title_case": false, "track_in_telemetry": false, "type": "str", "value": "" }, "base_url_ibm_watsonx": { "_input_type": "DropdownInput", "advanced": false, "combobox": false, "dialog_inputs": {}, "display_name": "watsonx API Endpoint", "dynamic": false, "external_options": {}, "info": "The base URL of the API (IBM watsonx.ai only)", "name": "base_url_ibm_watsonx", "options": [ "https://us-south.ml.cloud.ibm.com", "https://eu-de.ml.cloud.ibm.com", "https://eu-gb.ml.cloud.ibm.com", "https://au-syd.ml.cloud.ibm.com", "https://jp-tok.ml.cloud.ibm.com", "https://ca-tor.ml.cloud.ibm.com" ], "options_metadata": [], "override_skip": false, "placeholder": "", "real_time_refresh": true, "required": false, "show": false, "title_case": false, "toggle": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": true, "type": "str", "value": "https://us-south.ml.cloud.ibm.com" }, "code": { "advanced": true, "dynamic": true, "fileTypes": [], "file_path": "", "info": "", "list": false, "load_from_db": false, "multiline": true, "name": "code", "password": false, "placeholder": "", "required": true, "show": true, "title_case": false, "type": "code", "value": "from lfx.base.models.model import LCModelComponent\nfrom lfx.base.models.unified_models import (\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.field_typing.constants import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, StrInput\nfrom lfx.io import IntInput, MessageInput, ModelInput, MultilineInput, SecretStrInput, SliderInput\n\nDEFAULT_OLLAMA_URL = \"http://localhost:11434\"\n\n\nclass LanguageModelComponent(LCModelComponent):\n display_name = \"Language Model\"\n description = \"Runs a language model given a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-models\"\n icon = \"brain-circuit\"\n category = \"models\"\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n required=False,\n show=True,\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n StrInput(\n name=\"ollama_base_url\",\n display_name=\"Ollama API URL\",\n info=f\"Endpoint of the Ollama API (Ollama only). Defaults to {DEFAULT_OLLAMA_URL}\",\n value=DEFAULT_OLLAMA_URL,\n show=False,\n real_time_refresh=True,\n ),\n MessageInput(\n name=\"input_value\",\n display_name=\"Input\",\n info=\"The input text to send to the model\",\n ),\n MultilineInput(\n name=\"system_message\",\n display_name=\"System Message\",\n info=\"A system message that helps set the behavior of the assistant\",\n advanced=False,\n ),\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=\"Whether to stream the response\",\n value=False,\n advanced=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n info=\"Controls randomness in responses\",\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n ]\n\n def build_model(self) -> LanguageModel:\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=self.api_key,\n temperature=self.temperature,\n stream=self.stream,\n max_tokens=getattr(self, \"max_tokens\", None),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n ollama_base_url=getattr(self, \"ollama_base_url\", None),\n )\n\n def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n \"\"\"Dynamically update build config with user-filtered model options.\"\"\"\n return handle_model_input_update(self, build_config, field_value, field_name)\n" }, "input_value": { "_input_type": "MessageInput", "advanced": false, "display_name": "Input", "dynamic": false, "info": "The input text to send to the model", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": false, "name": "input_value", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "track_in_telemetry": false, "type": "str", "value": "" }, "is_refresh": false, "model": { "_input_type": "ModelInput", "advanced": false, "display_name": "Language Model", "dynamic": false, "external_options": { "fields": { "data": { "node": { "display_name": "Connect other models", "icon": "CornerDownLeft", "name": "connect_other_models" } } } }, "info": "Select your model provider", "input_types": [ "LanguageModel" ], "list": false, "list_add_label": "Add More", "model_type": "language", "name": "model", "options": [], "override_skip": false, "placeholder": "Setup Provider", "real_time_refresh": true, "refresh_button": true, "required": true, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "track_in_telemetry": false, "type": "model", "value": "" }, "ollama_base_url": { "_input_type": "MessageInput", "advanced": false, "display_name": "Ollama API URL", "dynamic": false, "info": "Endpoint of the Ollama API (Ollama only). Defaults to http://localhost:11434", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": true, "name": "ollama_base_url", "override_skip": false, "placeholder": "", "real_time_refresh": true, "required": false, "show": false, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "track_in_telemetry": false, "type": "str", "value": "http://localhost:11434" }, "project_id": { "_input_type": "StrInput", "advanced": false, "display_name": "watsonx Project ID", "dynamic": false, "info": "The project ID associated with the foundation model (IBM watsonx.ai only)", "list": false, "list_add_label": "Add More", "load_from_db": false, "name": "project_id", "override_skip": false, "placeholder": "", "required": false, "show": false, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": false, "type": "str", "value": "" }, "stream": { "_input_type": "BoolInput", "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Whether to stream the response", "list": false, "list_add_label": "Add More", "name": "stream", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": true, "type": "bool", "value": false }, "system_message": { "_input_type": "MultilineInput", "advanced": false, "ai_enabled": false, "copy_field": false, "display_name": "System Message", "dynamic": false, "info": "A system message that helps set the behavior of the assistant", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": false, "multiline": true, "name": "system_message", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "track_in_telemetry": false, "type": "str", "value": "" }, "temperature": { "_input_type": "SliderInput", "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "Controls randomness in responses", "max_label": "", "max_label_icon": "", "min_label": "", "min_label_icon": "", "name": "temperature", "override_skip": false, "placeholder": "", "range_spec": { "max": 1, "min": 0, "step": 0.01, "step_type": "float" }, "required": false, "show": true, "slider_buttons": false, "slider_buttons_options": [], "slider_input": false, "title_case": false, "tool_mode": false, "track_in_telemetry": false, "type": "slider", "value": 0.1 } }, "tool_mode": false }, "selected_output": "text_output", "showNode": true, "type": "LanguageModelComponent" }, "dragging": false, "id": "LanguageModelComponent-TZiUW", "measured": { "height": 531, "width": 320 }, "position": { "x": 1456.7250059464952, "y": 576.3277108797026 }, "selected": false, "type": "genericNode" }, { "data": { "id": "LanguageModelComponent-80mt4", "node": { "base_classes": [ "LanguageModel", "Message" ], "beta": false, "conditional_paths": [], "custom_fields": {}, "description": "Runs a language model given a specified provider.", "display_name": "Language Model", "documentation": "https://docs.langflow.org/components-models", "edited": false, "field_order": [ "model", "api_key", "base_url_ibm_watsonx", "project_id", "ollama_base_url", "input_value", "system_message", "stream", "temperature", "max_tokens" ], "frozen": false, "icon": "brain-circuit", "last_updated": "2025-12-11T21:29:10.202Z", "legacy": false, "lf_version": "1.7.0", "metadata": { "code_hash": "7fe3257f2169", "dependencies": { "dependencies": [ { "name": "lfx", "version": null } ], "total_dependencies": 1 }, "keywords": [ "model", "llm", "language model", "large language model" ], "module": "lfx.components.models_and_agents.language_model.LanguageModelComponent" }, "minimized": false, "output_types": [], "outputs": [ { "allows_loop": false, "cache": true, "display_name": "Model Response", "group_outputs": false, "loop_types": null, "method": "text_response", "name": "text_output", "options": null, "required_inputs": null, "selected": "Message", "tool_mode": true, "types": [ "Message" ], "value": "__UNDEFINED__" }, { "allows_loop": false, "cache": true, "display_name": "Language Model", "group_outputs": false, "loop_types": null, "method": "build_model", "name": "model_output", "options": null, "required_inputs": null, "selected": "LanguageModel", "tool_mode": true, "types": [ "LanguageModel" ], "value": "__UNDEFINED__" } ], "pinned": false, "priority": 0, "template": { "_frontend_node_flow_id": { "value": "524f71dc-cc58-441a-a009-c71d6755765f" }, "_frontend_node_folder_id": { "value": "afaca9ee-97e3-4211-8569-722a4058ea5e" }, "_type": "Component", "api_key": { "_input_type": "SecretStrInput", "advanced": true, "display_name": "API Key", "dynamic": false, "info": "Model Provider API key", "input_types": [], "load_from_db": false, "name": "api_key", "override_skip": false, "password": true, "placeholder": "", "real_time_refresh": true, "required": false, "show": true, "title_case": false, "track_in_telemetry": false, "type": "str", "value": "" }, "base_url_ibm_watsonx": { "_input_type": "DropdownInput", "advanced": false, "combobox": false, "dialog_inputs": {}, "display_name": "watsonx API Endpoint", "dynamic": false, "external_options": {}, "info": "The base URL of the API (IBM watsonx.ai only)", "name": "base_url_ibm_watsonx", "options": [ "https://us-south.ml.cloud.ibm.com", "https://eu-de.ml.cloud.ibm.com", "https://eu-gb.ml.cloud.ibm.com", "https://au-syd.ml.cloud.ibm.com", "https://jp-tok.ml.cloud.ibm.com", "https://ca-tor.ml.cloud.ibm.com" ], "options_metadata": [], "override_skip": false, "placeholder": "", "real_time_refresh": true, "required": false, "show": false, "title_case": false, "toggle": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": true, "type": "str", "value": "https://us-south.ml.cloud.ibm.com" }, "code": { "advanced": true, "dynamic": true, "fileTypes": [], "file_path": "", "info": "", "list": false, "load_from_db": false, "multiline": true, "name": "code", "password": false, "placeholder": "", "required": true, "show": true, "title_case": false, "type": "code", "value": "from lfx.base.models.model import LCModelComponent\nfrom lfx.base.models.unified_models import (\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.field_typing.constants import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, StrInput\nfrom lfx.io import IntInput, MessageInput, ModelInput, MultilineInput, SecretStrInput, SliderInput\n\nDEFAULT_OLLAMA_URL = \"http://localhost:11434\"\n\n\nclass LanguageModelComponent(LCModelComponent):\n display_name = \"Language Model\"\n description = \"Runs a language model given a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-models\"\n icon = \"brain-circuit\"\n category = \"models\"\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n required=False,\n show=True,\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n StrInput(\n name=\"ollama_base_url\",\n display_name=\"Ollama API URL\",\n info=f\"Endpoint of the Ollama API (Ollama only). Defaults to {DEFAULT_OLLAMA_URL}\",\n value=DEFAULT_OLLAMA_URL,\n show=False,\n real_time_refresh=True,\n ),\n MessageInput(\n name=\"input_value\",\n display_name=\"Input\",\n info=\"The input text to send to the model\",\n ),\n MultilineInput(\n name=\"system_message\",\n display_name=\"System Message\",\n info=\"A system message that helps set the behavior of the assistant\",\n advanced=False,\n ),\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=\"Whether to stream the response\",\n value=False,\n advanced=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n info=\"Controls randomness in responses\",\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n ]\n\n def build_model(self) -> LanguageModel:\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=self.api_key,\n temperature=self.temperature,\n stream=self.stream,\n max_tokens=getattr(self, \"max_tokens\", None),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n ollama_base_url=getattr(self, \"ollama_base_url\", None),\n )\n\n def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n \"\"\"Dynamically update build config with user-filtered model options.\"\"\"\n return handle_model_input_update(self, build_config, field_value, field_name)\n" }, "input_value": { "_input_type": "MessageInput", "advanced": false, "display_name": "Input", "dynamic": false, "info": "The input text to send to the model", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": false, "name": "input_value", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "track_in_telemetry": false, "type": "str", "value": "" }, "is_refresh": false, "model": { "_input_type": "ModelInput", "advanced": false, "display_name": "Language Model", "dynamic": false, "external_options": { "fields": { "data": { "node": { "display_name": "Connect other models", "icon": "CornerDownLeft", "name": "connect_other_models" } } } }, "info": "Select your model provider", "input_types": [ "LanguageModel" ], "list": false, "list_add_label": "Add More", "model_type": "language", "name": "model", "options": [], "override_skip": false, "placeholder": "Setup Provider", "real_time_refresh": true, "refresh_button": true, "required": true, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "track_in_telemetry": false, "type": "model", "value": "" }, "ollama_base_url": { "_input_type": "MessageInput", "advanced": false, "display_name": "Ollama API URL", "dynamic": false, "info": "Endpoint of the Ollama API (Ollama only). Defaults to http://localhost:11434", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": false, "name": "ollama_base_url", "override_skip": false, "placeholder": "", "real_time_refresh": true, "required": false, "show": false, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "track_in_telemetry": false, "type": "str", "value": "" }, "project_id": { "_input_type": "StrInput", "advanced": false, "display_name": "watsonx Project ID", "dynamic": false, "info": "The project ID associated with the foundation model (IBM watsonx.ai only)", "list": false, "list_add_label": "Add More", "load_from_db": false, "name": "project_id", "override_skip": false, "placeholder": "", "required": false, "show": false, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": false, "type": "str", "value": "" }, "stream": { "_input_type": "BoolInput", "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Whether to stream the response", "list": false, "list_add_label": "Add More", "name": "stream", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": true, "type": "bool", "value": false }, "system_message": { "_input_type": "MultilineInput", "advanced": false, "ai_enabled": false, "copy_field": false, "display_name": "System Message", "dynamic": false, "info": "A system message that helps set the behavior of the assistant", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": false, "multiline": true, "name": "system_message", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "track_in_telemetry": false, "type": "str", "value": "" }, "temperature": { "_input_type": "SliderInput", "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "Controls randomness in responses", "max_label": "", "max_label_icon": "", "min_label": "", "min_label_icon": "", "name": "temperature", "override_skip": false, "placeholder": "", "range_spec": { "max": 1, "min": 0, "step": 0.01, "step_type": "float" }, "required": false, "show": true, "slider_buttons": false, "slider_buttons_options": [], "slider_input": false, "title_case": false, "tool_mode": false, "track_in_telemetry": false, "type": "slider", "value": 0.1 } }, "tool_mode": false }, "selected_output": "text_output", "showNode": true, "type": "LanguageModelComponent" }, "dragging": false, "id": "LanguageModelComponent-80mt4", "measured": { "height": 531, "width": 320 }, "position": { "x": 2910.7639501961753, "y": 525.1670813598067 }, "selected": false, "type": "genericNode" }, { "data": { "id": "Agent-mIgZ5", "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": "https://docs.langflow.org/agents", "edited": false, "field_order": [ "model", "api_key", "base_url_ibm_watsonx", "project_id", "system_prompt", "context_id", "n_messages", "max_tokens", "format_instructions", "output_schema", "tools", "input_value", "handle_parsing_errors", "verbose", "max_iterations", "agent_description", "add_current_date_tool" ], "frozen": false, "icon": "bot", "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { "code_hash": "154c71cf7441", "dependencies": { "dependencies": [ { "name": "pydantic", "version": "2.12.5" }, { "name": "lfx", "version": null }, { "name": "langchain_core", "version": "1.2.28" } ], "total_dependencies": 3 }, "module": "lfx.components.models_and_agents.agent.AgentComponent" }, "minimized": false, "output_types": [], "outputs": [ { "allows_loop": false, "cache": true, "display_name": "Response", "group_outputs": false, "method": "message_response", "name": "response", "selected": "Message", "tool_mode": true, "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.", "input_types": [], "list": false, "list_add_label": "Add More", "name": "add_current_date_tool", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": true, "type": "bool", "value": true }, "agent_description": { "_input_type": "MultilineInput", "advanced": true, "ai_enabled": false, "copy_field": false, "display_name": "Agent Description [Deprecated]", "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. This feature is deprecated and will be removed in future versions.", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": false, "multiline": true, "name": "agent_description", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "track_in_telemetry": false, "type": "str", "value": "A helpful assistant with access to the following tools:" }, "api_key": { "_input_type": "SecretStrInput", "advanced": true, "display_name": "API Key", "dynamic": false, "info": "Overrides global provider settings. Leave blank to use your pre-configured API Key.", "input_types": [], "load_from_db": false, "name": "api_key", "override_skip": false, "password": true, "placeholder": "", "real_time_refresh": true, "required": false, "show": true, "title_case": false, "track_in_telemetry": false, "type": "str", "value": "" }, "base_url_ibm_watsonx": { "_input_type": "DropdownInput", "advanced": false, "combobox": true, "dialog_inputs": {}, "display_name": "watsonx API Endpoint", "dynamic": false, "external_options": {}, "info": "The base URL of the API (IBM watsonx.ai only)", "name": "base_url_ibm_watsonx", "options": [ "https://us-south.ml.cloud.ibm.com", "https://eu-de.ml.cloud.ibm.com", "https://eu-gb.ml.cloud.ibm.com", "https://au-syd.ml.cloud.ibm.com", "https://jp-tok.ml.cloud.ibm.com", "https://ca-tor.ml.cloud.ibm.com" ], "options_metadata": [], "override_skip": false, "placeholder": "", "real_time_refresh": true, "required": false, "show": false, "title_case": false, "toggle": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": true, "type": "str", "value": "https://us-south.ml.cloud.ibm.com" }, "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 __future__ import annotations\n\nimport json\nimport re\nfrom typing import TYPE_CHECKING\n\nfrom pydantic import ValidationError\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.helpers.base_model import build_model_from_schema\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\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 documentation: str = \"https://docs.langflow.org/agents\"\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 ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n 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 MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\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 = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n self.tools.append(current_date_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\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 result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n def _preprocess_schema(self, schema):\n \"\"\"Preprocess schema to ensure correct data types for build_model_from_schema.\"\"\"\n processed_schema = []\n for field in schema:\n processed_field = {\n \"name\": str(field.get(\"name\", \"field\")),\n \"type\": str(field.get(\"type\", \"str\")),\n \"description\": str(field.get(\"description\", \"\")),\n \"multiple\": field.get(\"multiple\", False),\n }\n # Ensure multiple is handled correctly\n if isinstance(processed_field[\"multiple\"], str):\n processed_field[\"multiple\"] = processed_field[\"multiple\"].lower() in [\n \"true\",\n \"1\",\n \"t\",\n \"y\",\n \"yes\",\n ]\n processed_schema.append(processed_field)\n return processed_schema\n\n async def build_structured_output_base(self, content: str):\n \"\"\"Build structured output with optional BaseModel validation.\"\"\"\n json_pattern = r\"\\{.*\\}\"\n schema_error_msg = \"Try setting an output schema\"\n\n # Try to parse content as JSON first\n json_data = None\n try:\n json_data = json.loads(content)\n except json.JSONDecodeError:\n json_match = re.search(json_pattern, content, re.DOTALL)\n if json_match:\n try:\n json_data = json.loads(json_match.group())\n except json.JSONDecodeError:\n return {\"content\": content, \"error\": schema_error_msg}\n else:\n return {\"content\": content, \"error\": schema_error_msg}\n\n # If no output schema provided, return parsed JSON without validation\n if not hasattr(self, \"output_schema\") or not self.output_schema or len(self.output_schema) == 0:\n return json_data\n\n # Use BaseModel validation with schema\n try:\n processed_schema = self._preprocess_schema(self.output_schema)\n output_model = build_model_from_schema(processed_schema)\n\n # Validate against the schema\n if isinstance(json_data, list):\n # Multiple objects\n validated_objects = []\n for item in json_data:\n try:\n validated_obj = output_model.model_validate(item)\n validated_objects.append(validated_obj.model_dump())\n except ValidationError as e:\n await logger.aerror(f\"Validation error for item: {e}\")\n # Include invalid items with error info\n validated_objects.append({\"data\": item, \"validation_error\": str(e)})\n return validated_objects\n\n # Single object\n try:\n validated_obj = output_model.model_validate(json_data)\n return [validated_obj.model_dump()] # Return as list for consistency\n except ValidationError as e:\n await logger.aerror(f\"Validation error: {e}\")\n return [{\"data\": json_data, \"validation_error\": str(e)}]\n\n except (TypeError, ValueError) as e:\n await logger.aerror(f\"Error building structured output: {e}\")\n # Fallback to parsed JSON without validation\n return json_data\n\n async def json_response(self) -> Data:\n \"\"\"Convert agent response to structured JSON Data output with schema validation.\"\"\"\n # Always use structured chat agent for JSON response mode for better JSON formatting\n try:\n system_components = []\n\n # 1. Agent Instructions (system_prompt)\n agent_instructions = getattr(self, \"system_prompt\", \"\") or \"\"\n if agent_instructions:\n system_components.append(f\"{agent_instructions}\")\n\n # 2. Format Instructions\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n if format_instructions:\n system_components.append(f\"Format instructions: {format_instructions}\")\n\n # 3. Schema Information from BaseModel\n if hasattr(self, \"output_schema\") and self.output_schema and len(self.output_schema) > 0:\n try:\n processed_schema = self._preprocess_schema(self.output_schema)\n output_model = build_model_from_schema(processed_schema)\n schema_dict = output_model.model_json_schema()\n schema_info = (\n \"You are given some text that may include format instructions, \"\n \"explanations, or other content alongside a JSON schema.\\n\\n\"\n \"Your task:\\n\"\n \"- Extract only the JSON schema.\\n\"\n \"- Return it as valid JSON.\\n\"\n \"- Do not include format instructions, explanations, or extra text.\\n\\n\"\n \"Input:\\n\"\n f\"{json.dumps(schema_dict, indent=2)}\\n\\n\"\n \"Output (only JSON schema):\"\n )\n system_components.append(schema_info)\n except (ValidationError, ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"Could not build schema for prompt: {e}\", exc_info=True)\n\n # Combine all components\n combined_instructions = \"\\n\\n\".join(system_components) if system_components else \"\"\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=combined_instructions,\n )\n\n # Create and run structured chat agent\n try:\n structured_agent = self.create_agent_runnable()\n except (NotImplementedError, ValueError, TypeError) as e:\n await logger.aerror(f\"Error with structured chat agent: {e}\")\n raise\n try:\n result = await self.run_agent(structured_agent)\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n RuntimeError,\n ) as e:\n await logger.aerror(f\"Error with structured agent result: {e}\")\n raise\n # Extract content from structured agent result\n if hasattr(result, \"content\"):\n content = result.content\n elif hasattr(result, \"text\"):\n content = result.text\n else:\n content = str(result)\n\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as e:\n await logger.aerror(f\"Error with structured chat agent: {e}\")\n # Fallback to regular agent\n content_str = \"No content returned from agent\"\n return Data(data={\"content\": content_str, \"error\": str(e)})\n\n # Process with structured output validation\n try:\n structured_output = await self.build_structured_output_base(content)\n\n # Handle different output formats\n if isinstance(structured_output, list) and structured_output:\n if len(structured_output) == 1:\n return Data(data=structured_output[0])\n return Data(data={\"results\": structured_output})\n if isinstance(structured_output, dict):\n return Data(data=structured_output)\n return Data(data={\"content\": content})\n\n except (ValueError, TypeError) as e:\n await logger.aerror(f\"Error in structured output processing: {e}\")\n return Data(data={\"content\": content, \"error\": str(e)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\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 async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes)\n # Agents require tool calling, so filter for only tool-calling capable models\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n cache_key_prefix=\"language_model_options_tool_calling\",\n get_options_func=lambda user_id=None: get_language_model_options(user_id=user_id, tool_calling=True),\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\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 return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", "advanced": true, "display_name": "Context ID", "dynamic": false, "info": "The context ID of the chat. Adds an extra layer to the local memory.", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": false, "name": "context_id", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "track_in_telemetry": false, "type": "str", "value": "" }, "format_instructions": { "_input_type": "MultilineInput", "advanced": true, "ai_enabled": false, "copy_field": false, "display_name": "Output Format Instructions", "dynamic": false, "info": "Generic Template for structured output formatting. Valid only with Structured response.", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": false, "multiline": true, "name": "format_instructions", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "track_in_telemetry": false, "type": "str", "value": "You are an AI that extracts structured JSON objects from unstructured text. Use a predefined schema with expected types (str, int, float, bool, dict). Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. Fill missing or ambiguous values with defaults: null for missing values. Remove exact duplicates but keep variations that have different field values. Always return valid JSON in the expected format, never throw errors. If multiple objects can be extracted, return them all in the structured format." }, "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?", "input_types": [], "list": false, "list_add_label": "Add More", "name": "handle_parsing_errors", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": true, "type": "bool", "value": true }, "input_value": { "_input_type": "MessageInput", "advanced": false, "display_name": "Input", "dynamic": false, "info": "The input provided by the user for the agent to process.", "input_types": [ "Message" ], "list": false, "list_add_label": "Add More", "load_from_db": false, "name": "input_value", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": true, "trace_as_input": true, "trace_as_metadata": true, "track_in_telemetry": false, "type": "str", "value": "" }, "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.", "input_types": [], "list": false, "list_add_label": "Add More", "name": "max_iterations", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": true, "type": "int", "value": 15 }, "max_tokens": { "_input_type": "IntInput", "advanced": true, "display_name": "Max Tokens", "dynamic": false, "info": "Maximum number of tokens to generate. Field name varies by provider.", "list": false, "list_add_label": "Add More", "name": "max_tokens", "override_skip": false, "placeholder": "", "range_spec": { "max": 128000.0, "min": 1.0, "step": 1.0, "step_type": "int" }, "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": true, "type": "int", "value": 0 }, "model": { "_input_type": "ModelInput", "advanced": false, "display_name": "Language Model", "dynamic": false, "external_options": { "fields": { "data": { "node": { "display_name": "Connect other models", "icon": "CornerDownLeft", "name": "connect_other_models" } } } }, "info": "Select your model provider", "input_types": [ "LanguageModel" ], "list": false, "list_add_label": "Add More", "model_type": "language", "name": "model", "options": [], "override_skip": false, "placeholder": "Setup Provider", "real_time_refresh": true, "refresh_button": true, "required": true, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "track_in_telemetry": false, "type": "model", "value": "" }, "n_messages": { "_input_type": "IntInput", "advanced": true, "display_name": "Number of Chat History Messages", "dynamic": false, "info": "Number of chat history messages to retrieve.", "input_types": [], "list": false, "list_add_label": "Add More", "name": "n_messages", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": true, "type": "int", "value": 100 }, "output_schema": { "_input_type": "TableInput", "advanced": true, "display_name": "Output Schema", "dynamic": false, "info": "Schema Validation: Define the structure and data types for structured output. No validation if no output schema.", "input_types": [ "DataFrame", "Table" ], "is_list": true, "list_add_label": "Add More", "name": "output_schema", "override_skip": false, "placeholder": "", "required": false, "show": true, "table_icon": "Table", "table_schema": [ { "default": "field", "description": "Specify the name of the output field.", "display_name": "Name", "edit_mode": "inline", "name": "name", "type": "str" }, { "default": "description of field", "description": "Describe the purpose of the output field.", "display_name": "Description", "edit_mode": "popover", "name": "description", "type": "str" }, { "default": "str", "description": "Indicate the data type of the output field (e.g., str, int, float, bool, dict).", "display_name": "Type", "edit_mode": "inline", "name": "type", "options": [ "str", "int", "float", "bool", "dict" ], "type": "str" }, { "default": "False", "description": "Set to True if this output field should be a list of the specified type.", "display_name": "As List", "edit_mode": "inline", "name": "multiple", "type": "boolean" } ], "title_case": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": false, "trigger_icon": "Table", "trigger_text": "Open table", "type": "table", "value": [] }, "project_id": { "_input_type": "StrInput", "advanced": false, "display_name": "watsonx Project ID", "dynamic": false, "info": "The project ID associated with the foundation model (IBM watsonx.ai only)", "list": false, "list_add_label": "Add More", "load_from_db": false, "name": "project_id", "override_skip": false, "placeholder": "", "required": false, "show": false, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": false, "type": "str", "value": "" }, "system_prompt": { "_input_type": "MultilineInput", "advanced": false, "ai_enabled": false, "copy_field": 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, "list_add_label": "Add More", "load_from_db": false, "multiline": true, "name": "system_prompt", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, "track_in_telemetry": false, "type": "str", "value": "You are a helpful assistant that can use tools to answer questions and perform tasks." }, "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" ], "list": true, "list_add_label": "Add More", "name": "tools", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "trace_as_metadata": true, "track_in_telemetry": false, "type": "other", "value": "" }, "verbose": { "_input_type": "BoolInput", "advanced": true, "display_name": "Verbose", "dynamic": false, "info": "", "input_types": [], "list": false, "list_add_label": "Add More", "name": "verbose", "override_skip": false, "placeholder": "", "required": false, "show": true, "title_case": false, "tool_mode": false, "trace_as_metadata": true, "track_in_telemetry": true, "type": "bool", "value": true } }, "tool_mode": false }, "showNode": true, "type": "Agent" }, "dragging": false, "id": "Agent-mIgZ5", "measured": { "height": 591, "width": 320 }, "position": { "x": 2160.8568280229615, "y": 389.1875134253461 }, "selected": false, "type": "genericNode" } ], "viewport": { "x": -466.9165258064488, "y": -1.965877408958704, "zoom": 0.8435310976168224 } }, "description": "Agent that generates focused plans, conducts web searches, and synthesizes findings into comprehensive reports.", "endpoint_name": null, "id": "d482ec13-1d17-4be1-a904-5b7ad55619fe", "is_component": false, "last_tested_version": "1.4.3", "name": "Research Agent", "tags": [ "assistants", "agents" ] }