843fc92b76
Updated the OpenSearchVectorStoreComponent to improve document metadata ingestion, including support for Data objects in docs_metadata. Added new edges and nodes to ingestion_flow.json for dynamic metadata input. Changed Dockerfile.langflow to use the fix-file-component branch.
2740 lines
No EOL
174 KiB
JSON
2740 lines
No EOL
174 KiB
JSON
{
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"data": {
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"edges": [
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{
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"animated": false,
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"className": "",
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"data": {
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"sourceHandle": {
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"dataType": "EmbeddingModel",
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"id": "EmbeddingModel-eZ6bT",
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"name": "embeddings",
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"output_types": [
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"Embeddings"
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]
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},
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"targetHandle": {
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"fieldName": "embedding",
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"id": "OpenSearch-iYfjf",
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"inputTypes": [
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"Embeddings"
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],
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"type": "other"
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}
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},
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"id": "xy-edge__EmbeddingModel-eZ6bT{œdataTypeœ:œEmbeddingModelœ,œidœ:œEmbeddingModel-eZ6bTœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}-OpenSearch-iYfjf{œfieldNameœ:œembeddingœ,œidœ:œOpenSearch-iYfjfœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}",
|
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"selected": false,
|
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"source": "EmbeddingModel-eZ6bT",
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"sourceHandle": "{œdataTypeœ:œEmbeddingModelœ,œidœ:œEmbeddingModel-eZ6bTœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}",
|
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"target": "OpenSearch-iYfjf",
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"targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œOpenSearch-iYfjfœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}"
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},
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{
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"animated": false,
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"className": "",
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"data": {
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"sourceHandle": {
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"dataType": "ChatInput",
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"id": "ChatInput-bqH7H",
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"name": "message",
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"output_types": [
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"Message"
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]
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},
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"targetHandle": {
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"fieldName": "input_value",
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"id": "Agent-crjWf",
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"inputTypes": [
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"Message"
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],
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"type": "str"
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}
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},
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"id": "xy-edge__ChatInput-bqH7H{œdataTypeœ:œChatInputœ,œidœ:œChatInput-bqH7Hœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Agent-crjWf{œfieldNameœ:œinput_valueœ,œidœ:œAgent-crjWfœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
|
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"selected": false,
|
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"source": "ChatInput-bqH7H",
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"sourceHandle": "{œdataTypeœ:œChatInputœ,œidœ:œChatInput-bqH7Hœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}",
|
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"target": "Agent-crjWf",
|
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"targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œAgent-crjWfœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}"
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},
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{
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"animated": false,
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"className": "",
|
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"data": {
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"sourceHandle": {
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"dataType": "Agent",
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"id": "Agent-crjWf",
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"name": "response",
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"output_types": [
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"Message"
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]
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},
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"targetHandle": {
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"fieldName": "input_value",
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"id": "ChatOutput-BMVN5",
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"inputTypes": [
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"Data",
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"DataFrame",
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"Message"
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],
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"type": "other"
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}
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},
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"id": "xy-edge__Agent-crjWf{œdataTypeœ:œAgentœ,œidœ:œAgent-crjWfœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-BMVN5{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-BMVN5œ,œinputTypesœ:[œDataœ,œDataFrameœ,œMessageœ],œtypeœ:œotherœ}",
|
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"selected": false,
|
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"source": "Agent-crjWf",
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"sourceHandle": "{œdataTypeœ:œAgentœ,œidœ:œAgent-crjWfœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}",
|
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"target": "ChatOutput-BMVN5",
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"targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-BMVN5œ,œinputTypesœ:[œDataœ,œDataFrameœ,œMessageœ],œtypeœ:œotherœ}"
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},
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{
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"animated": false,
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"className": "",
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"data": {
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"sourceHandle": {
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"dataType": "OpenSearchVectorStoreComponent",
|
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"id": "OpenSearch-iYfjf",
|
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"name": "component_as_tool",
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"output_types": [
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"Tool"
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]
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},
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"targetHandle": {
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"fieldName": "tools",
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"id": "Agent-crjWf",
|
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"inputTypes": [
|
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"Tool"
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],
|
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"type": "other"
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}
|
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},
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"id": "xy-edge__OpenSearch-iYfjf{œdataTypeœ:œOpenSearchVectorStoreComponentœ,œidœ:œOpenSearch-iYfjfœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}-Agent-crjWf{œfieldNameœ:œtoolsœ,œidœ:œAgent-crjWfœ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
|
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"selected": false,
|
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"source": "OpenSearch-iYfjf",
|
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"sourceHandle": "{œdataTypeœ:œOpenSearchVectorStoreComponentœ,œidœ:œOpenSearch-iYfjfœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}",
|
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"target": "Agent-crjWf",
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"targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œAgent-crjWfœ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}"
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}
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],
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"nodes": [
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{
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"data": {
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"id": "ChatInput-bqH7H",
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"node": {
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"base_classes": [
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"Message"
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],
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"beta": false,
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"conditional_paths": [],
|
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"custom_fields": {},
|
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"description": "Get chat inputs from the Playground.",
|
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"display_name": "Chat Input",
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"documentation": "https://docs.langflow.org/components-io#chat-input",
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"edited": false,
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"field_order": [
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"input_value",
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"should_store_message",
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"sender",
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"sender_name",
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"session_id",
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"files"
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],
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"frozen": false,
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"icon": "MessagesSquare",
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"legacy": false,
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"lf_version": "1.6.0",
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"metadata": {
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"code_hash": "46a90558cb44",
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"dependencies": {
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"dependencies": [
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{
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"name": "lfx",
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"version": null
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}
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],
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"total_dependencies": 1
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},
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"module": "custom_components.chat_input"
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},
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"minimized": true,
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"output_types": [],
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"outputs": [
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{
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"allows_loop": false,
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"cache": true,
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"display_name": "Chat Message",
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"group_outputs": false,
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"method": "message_response",
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"name": "message",
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"options": null,
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"required_inputs": null,
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"selected": "Message",
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"tool_mode": true,
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"types": [
|
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"Message"
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],
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"value": "__UNDEFINED__"
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}
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],
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"pinned": false,
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"template": {
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"_type": "Component",
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"code": {
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"advanced": true,
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"dynamic": true,
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"fileTypes": [],
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"file_path": "",
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"info": "",
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"list": false,
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"load_from_db": false,
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"multiline": true,
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"name": "code",
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"password": false,
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"placeholder": "",
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"required": true,
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"show": true,
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"title_case": false,
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"type": "code",
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"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/components-io#chat-input\"\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 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 message = await Message.create(\n text=self.input_value,\n sender=self.sender,\n sender_name=self.sender_name,\n session_id=self.session_id,\n files=self.files,\n )\n if self.session_id and isinstance(message, Message) and self.should_store_message:\n stored_message = await self.send_message(\n message,\n )\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n"
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},
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"files": {
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"_input_type": "FileInput",
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"advanced": true,
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"display_name": "Files",
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"dynamic": false,
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"fileTypes": [
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"csv",
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"json",
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"pdf",
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"txt",
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"md",
|
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"mdx",
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"yaml",
|
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"yml",
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"xml",
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"html",
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"htm",
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"docx",
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"py",
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"sh",
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"sql",
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"js",
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"ts",
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"tsx",
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"jpg",
|
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"jpeg",
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"png",
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"bmp",
|
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"image"
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],
|
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"file_path": "",
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"info": "Files to be sent with the message.",
|
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"list": true,
|
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"list_add_label": "Add More",
|
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"name": "files",
|
|
"placeholder": "",
|
|
"required": false,
|
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"show": true,
|
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"temp_file": true,
|
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"title_case": false,
|
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"trace_as_metadata": true,
|
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"type": "file",
|
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"value": ""
|
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},
|
|
"input_value": {
|
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"_input_type": "MultilineInput",
|
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"advanced": false,
|
|
"copy_field": false,
|
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"display_name": "Input Text",
|
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"dynamic": false,
|
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"info": "Message to be passed as input.",
|
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"input_types": [],
|
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"list": false,
|
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"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"multiline": true,
|
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"name": "input_value",
|
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"placeholder": "",
|
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"required": false,
|
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"show": true,
|
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"title_case": false,
|
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"tool_mode": false,
|
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"trace_as_input": true,
|
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"trace_as_metadata": true,
|
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"type": "str",
|
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"value": ""
|
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},
|
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"sender": {
|
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"_input_type": "DropdownInput",
|
|
"advanced": true,
|
|
"combobox": false,
|
|
"dialog_inputs": {},
|
|
"display_name": "Sender Type",
|
|
"dynamic": false,
|
|
"external_options": {},
|
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"info": "Type of sender.",
|
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"name": "sender",
|
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"options": [
|
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"Machine",
|
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"User"
|
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],
|
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"options_metadata": [],
|
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"placeholder": "",
|
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"required": false,
|
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"show": true,
|
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"title_case": false,
|
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"toggle": false,
|
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"tool_mode": false,
|
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"trace_as_metadata": true,
|
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"type": "str",
|
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"value": "User"
|
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},
|
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"sender_name": {
|
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"_input_type": "MessageTextInput",
|
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"advanced": true,
|
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"display_name": "Sender Name",
|
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"dynamic": false,
|
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"info": "Name of the sender.",
|
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"input_types": [
|
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"Message"
|
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],
|
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"list": false,
|
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"list_add_label": "Add More",
|
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"load_from_db": false,
|
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"name": "sender_name",
|
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"placeholder": "",
|
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"required": false,
|
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"show": true,
|
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"title_case": false,
|
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"tool_mode": false,
|
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"trace_as_input": true,
|
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"trace_as_metadata": true,
|
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"type": "str",
|
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"value": "User"
|
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},
|
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"session_id": {
|
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"_input_type": "MessageTextInput",
|
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"advanced": true,
|
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"display_name": "Session ID",
|
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"dynamic": false,
|
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"info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
|
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"input_types": [
|
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"Message"
|
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],
|
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"list": false,
|
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"list_add_label": "Add More",
|
|
"load_from_db": false,
|
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"name": "session_id",
|
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"placeholder": "",
|
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"required": false,
|
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"show": true,
|
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"title_case": false,
|
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"tool_mode": false,
|
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"trace_as_input": true,
|
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"trace_as_metadata": true,
|
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"type": "str",
|
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"value": ""
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},
|
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"should_store_message": {
|
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"_input_type": "BoolInput",
|
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"advanced": true,
|
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"display_name": "Store Messages",
|
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"dynamic": false,
|
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"info": "Store the message in the history.",
|
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"list": false,
|
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"list_add_label": "Add More",
|
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"name": "should_store_message",
|
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"placeholder": "",
|
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"required": false,
|
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"show": true,
|
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"title_case": false,
|
|
"tool_mode": false,
|
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"trace_as_metadata": true,
|
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"type": "bool",
|
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"value": true
|
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}
|
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},
|
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"tool_mode": false
|
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},
|
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"selected_output": "message",
|
|
"showNode": false,
|
|
"type": "ChatInput"
|
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},
|
|
"dragging": false,
|
|
"id": "ChatInput-bqH7H",
|
|
"measured": {
|
|
"height": 48,
|
|
"width": 192
|
|
},
|
|
"position": {
|
|
"x": 1264.0651279011304,
|
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"y": 1192.017532447814
|
|
},
|
|
"selected": false,
|
|
"type": "genericNode"
|
|
},
|
|
{
|
|
"data": {
|
|
"id": "ChatOutput-BMVN5",
|
|
"node": {
|
|
"base_classes": [
|
|
"Message"
|
|
],
|
|
"beta": false,
|
|
"conditional_paths": [],
|
|
"custom_fields": {},
|
|
"description": "Display a chat message in the Playground.",
|
|
"display_name": "Chat Output",
|
|
"documentation": "https://docs.langflow.org/components-io#chat-output",
|
|
"edited": false,
|
|
"field_order": [
|
|
"input_value",
|
|
"should_store_message",
|
|
"sender",
|
|
"sender_name",
|
|
"session_id",
|
|
"data_template",
|
|
"clean_data"
|
|
],
|
|
"frozen": false,
|
|
"icon": "MessagesSquare",
|
|
"legacy": false,
|
|
"lf_version": "1.6.0",
|
|
"metadata": {
|
|
"code_hash": "ccda4dbe4ae1",
|
|
"dependencies": {
|
|
"dependencies": [
|
|
{
|
|
"name": "orjson",
|
|
"version": "3.10.15"
|
|
},
|
|
{
|
|
"name": "fastapi",
|
|
"version": "0.116.1"
|
|
},
|
|
{
|
|
"name": "lfx",
|
|
"version": null
|
|
}
|
|
],
|
|
"total_dependencies": 3
|
|
},
|
|
"module": "custom_components.chat_output"
|
|
},
|
|
"minimized": true,
|
|
"output_types": [],
|
|
"outputs": [
|
|
{
|
|
"allows_loop": false,
|
|
"cache": true,
|
|
"display_name": "Output Message",
|
|
"group_outputs": false,
|
|
"method": "message_response",
|
|
"name": "message",
|
|
"options": null,
|
|
"required_inputs": null,
|
|
"selected": "Message",
|
|
"tool_mode": true,
|
|
"types": [
|
|
"Message"
|
|
],
|
|
"value": "__UNDEFINED__"
|
|
}
|
|
],
|
|
"pinned": false,
|
|
"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/components-io#chat-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\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n 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, icon, 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):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _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"
|
|
},
|
|
"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",
|
|
"DataFrame",
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "input_value",
|
|
"placeholder": "",
|
|
"required": true,
|
|
"show": true,
|
|
"title_case": false,
|
|
"trace_as_metadata": true,
|
|
"type": "other",
|
|
"value": ""
|
|
},
|
|
"sender": {
|
|
"_input_type": "DropdownInput",
|
|
"advanced": true,
|
|
"combobox": false,
|
|
"dialog_inputs": {},
|
|
"display_name": "Sender Type",
|
|
"dynamic": false,
|
|
"external_options": {},
|
|
"info": "Type of sender.",
|
|
"name": "sender",
|
|
"options": [
|
|
"Machine",
|
|
"User"
|
|
],
|
|
"options_metadata": [],
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"toggle": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "Machine"
|
|
},
|
|
"sender_name": {
|
|
"_input_type": "MessageTextInput",
|
|
"advanced": true,
|
|
"display_name": "Sender Name",
|
|
"dynamic": false,
|
|
"info": "Name of the sender.",
|
|
"input_types": [
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "sender_name",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "AI"
|
|
},
|
|
"session_id": {
|
|
"_input_type": "MessageTextInput",
|
|
"advanced": true,
|
|
"display_name": "Session ID",
|
|
"dynamic": false,
|
|
"info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
|
|
"input_types": [
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "session_id",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"should_store_message": {
|
|
"_input_type": "BoolInput",
|
|
"advanced": true,
|
|
"display_name": "Store Messages",
|
|
"dynamic": false,
|
|
"info": "Store the message in the history.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "should_store_message",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "bool",
|
|
"value": true
|
|
}
|
|
},
|
|
"tool_mode": false
|
|
},
|
|
"showNode": false,
|
|
"type": "ChatOutput"
|
|
},
|
|
"id": "ChatOutput-BMVN5",
|
|
"measured": {
|
|
"height": 48,
|
|
"width": 192
|
|
},
|
|
"position": {
|
|
"x": 2145,
|
|
"y": 660
|
|
},
|
|
"selected": false,
|
|
"type": "genericNode"
|
|
},
|
|
{
|
|
"data": {
|
|
"id": "OpenSearch-iYfjf",
|
|
"node": {
|
|
"base_classes": [
|
|
"Data",
|
|
"DataFrame",
|
|
"VectorStore"
|
|
],
|
|
"beta": false,
|
|
"conditional_paths": [],
|
|
"custom_fields": {},
|
|
"description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
|
|
"display_name": "OpenSearch",
|
|
"documentation": "",
|
|
"edited": true,
|
|
"field_order": [
|
|
"docs_metadata",
|
|
"opensearch_url",
|
|
"index_name",
|
|
"engine",
|
|
"space_type",
|
|
"ef_construction",
|
|
"m",
|
|
"ingest_data",
|
|
"search_query",
|
|
"should_cache_vector_store",
|
|
"embedding",
|
|
"vector_field",
|
|
"number_of_results",
|
|
"filter_expression",
|
|
"auth_mode",
|
|
"username",
|
|
"password",
|
|
"jwt_token",
|
|
"jwt_header",
|
|
"bearer_prefix",
|
|
"use_ssl",
|
|
"verify_certs"
|
|
],
|
|
"frozen": false,
|
|
"icon": "OpenSearch",
|
|
"last_updated": "2025-09-26T05:15:05.779Z",
|
|
"legacy": false,
|
|
"lf_version": "1.6.0",
|
|
"metadata": {
|
|
"code_hash": "b19e82f1314a",
|
|
"dependencies": {
|
|
"dependencies": [
|
|
{
|
|
"name": "opensearchpy",
|
|
"version": "2.8.0"
|
|
},
|
|
{
|
|
"name": "lfx",
|
|
"version": null
|
|
}
|
|
],
|
|
"total_dependencies": 2
|
|
},
|
|
"module": "custom_components.opensearch"
|
|
},
|
|
"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",
|
|
"auth_mode": {
|
|
"_input_type": "DropdownInput",
|
|
"advanced": false,
|
|
"combobox": false,
|
|
"dialog_inputs": {},
|
|
"display_name": "Authentication Mode",
|
|
"dynamic": false,
|
|
"external_options": {},
|
|
"info": "Authentication method: 'basic' for username/password authentication, or 'jwt' for JSON Web Token (Bearer) authentication.",
|
|
"load_from_db": false,
|
|
"name": "auth_mode",
|
|
"options": [
|
|
"basic",
|
|
"jwt"
|
|
],
|
|
"options_metadata": [],
|
|
"placeholder": "",
|
|
"real_time_refresh": true,
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"toggle": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "jwt"
|
|
},
|
|
"bearer_prefix": {
|
|
"_input_type": "BoolInput",
|
|
"advanced": true,
|
|
"display_name": "Prefix 'Bearer '",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "bearer_prefix",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": false,
|
|
"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 __future__ import annotations\n\nimport json\nimport uuid\nfrom typing import Any\n\nfrom opensearchpy import OpenSearch, helpers\n\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.base.vectorstores.vector_store_connection_decorator import vector_store_connection\nfrom lfx.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput, TableInput\nfrom lfx.log import logger\nfrom lfx.schema.data import Data\n\n\n@vector_store_connection\nclass OpenSearchVectorStoreComponent(LCVectorStoreComponent):\n \"\"\"OpenSearch Vector Store Component with Hybrid Search Capabilities.\n\n This component provides vector storage and retrieval using OpenSearch, combining semantic\n similarity search (KNN) with keyword-based search for optimal results. It supports document\n ingestion, vector embeddings, and advanced filtering with authentication options.\n\n Features:\n - Vector storage with configurable engines (jvector, nmslib, faiss, lucene)\n - Hybrid search combining KNN vector similarity and keyword matching\n - Flexible authentication (Basic auth, JWT tokens)\n - Advanced filtering and aggregations\n - Metadata injection during document ingestion\n \"\"\"\n\n display_name: str = \"OpenSearch\"\n icon: str = \"OpenSearch\"\n description: str = (\n \"Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.\"\n )\n\n # Keys we consider baseline\n default_keys: list[str] = [\n \"opensearch_url\",\n \"index_name\",\n *[i.name for i in LCVectorStoreComponent.inputs], # search_query, add_documents, etc.\n \"embedding\",\n \"vector_field\",\n \"number_of_results\",\n \"auth_mode\",\n \"username\",\n \"password\",\n \"jwt_token\",\n \"jwt_header\",\n \"bearer_prefix\",\n \"use_ssl\",\n \"verify_certs\",\n \"filter_expression\",\n \"engine\",\n \"space_type\",\n \"ef_construction\",\n \"m\",\n \"docs_metadata\",\n ]\n\n inputs = [\n TableInput(\n name=\"docs_metadata\",\n display_name=\"Document Metadata\",\n info=(\n \"Additional metadata key-value pairs to be added to all ingested documents. \"\n \"Useful for tagging documents with source information, categories, or other custom attributes.\"\n ),\n table_schema=[\n {\n \"name\": \"key\",\n \"display_name\": \"Key\",\n \"type\": \"str\",\n \"description\": \"Key name\",\n },\n {\n \"name\": \"value\",\n \"display_name\": \"Value\",\n \"type\": \"str\",\n \"description\": \"Value of the metadata\",\n },\n ],\n value=[],\n advanced=True,\n ),\n StrInput(\n name=\"opensearch_url\",\n display_name=\"OpenSearch URL\",\n value=\"http://localhost:9200\",\n info=(\n \"The connection URL for your OpenSearch cluster \"\n \"(e.g., http://localhost:9200 for local development or your cloud endpoint).\"\n ),\n ),\n StrInput(\n name=\"index_name\",\n display_name=\"Index Name\",\n value=\"langflow\",\n info=(\n \"The OpenSearch index name where documents will be stored and searched. \"\n \"Will be created automatically if it doesn't exist.\"\n ),\n ),\n DropdownInput(\n name=\"engine\",\n display_name=\"Vector Engine\",\n options=[\"jvector\", \"nmslib\", \"faiss\", \"lucene\"],\n value=\"jvector\",\n info=(\n \"Vector search engine for similarity calculations. 'jvector' is recommended for most use cases. \"\n \"Note: Amazon OpenSearch Serverless only supports 'nmslib' or 'faiss'.\"\n ),\n advanced=True,\n ),\n DropdownInput(\n name=\"space_type\",\n display_name=\"Distance Metric\",\n options=[\"l2\", \"l1\", \"cosinesimil\", \"linf\", \"innerproduct\"],\n value=\"l2\",\n info=(\n \"Distance metric for calculating vector similarity. 'l2' (Euclidean) is most common, \"\n \"'cosinesimil' for cosine similarity, 'innerproduct' for dot product.\"\n ),\n advanced=True,\n ),\n IntInput(\n name=\"ef_construction\",\n display_name=\"EF Construction\",\n value=512,\n info=(\n \"Size of the dynamic candidate list during index construction. \"\n \"Higher values improve recall but increase indexing time and memory usage.\"\n ),\n advanced=True,\n ),\n IntInput(\n name=\"m\",\n display_name=\"M Parameter\",\n value=16,\n info=(\n \"Number of bidirectional connections for each vector in the HNSW graph. \"\n \"Higher values improve search quality but increase memory usage and indexing time.\"\n ),\n advanced=True,\n ),\n *LCVectorStoreComponent.inputs, # includes search_query, add_documents, etc.\n HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\n StrInput(\n name=\"vector_field\",\n display_name=\"Vector Field Name\",\n value=\"chunk_embedding\",\n advanced=True,\n info=\"Name of the field in OpenSearch documents that stores the vector embeddings for similarity search.\",\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Default Result Limit\",\n value=10,\n advanced=True,\n info=(\n \"Default maximum number of search results to return when no limit is \"\n \"specified in the filter expression.\"\n ),\n ),\n MultilineInput(\n name=\"filter_expression\",\n display_name=\"Search Filters (JSON)\",\n value=\"\",\n info=(\n \"Optional JSON configuration for search filtering, result limits, and score thresholds.\\n\\n\"\n \"Format 1 - Explicit filters:\\n\"\n '{\"filter\": [{\"term\": {\"filename\":\"doc.pdf\"}}, '\n '{\"terms\":{\"owner\":[\"user1\",\"user2\"]}}], \"limit\": 10, \"score_threshold\": 1.6}\\n\\n'\n \"Format 2 - Context-style mapping:\\n\"\n '{\"data_sources\":[\"file.pdf\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"user123\"]}\\n\\n'\n \"Use __IMPOSSIBLE_VALUE__ as placeholder to ignore specific filters.\"\n ),\n ),\n # ----- Auth controls (dynamic) -----\n DropdownInput(\n name=\"auth_mode\",\n display_name=\"Authentication Mode\",\n value=\"basic\",\n options=[\"basic\", \"jwt\"],\n info=(\n \"Authentication method: 'basic' for username/password authentication, \"\n \"or 'jwt' for JSON Web Token (Bearer) authentication.\"\n ),\n real_time_refresh=True,\n advanced=False,\n ),\n StrInput(\n name=\"username\",\n display_name=\"Username\",\n value=\"admin\",\n show=False,\n ),\n SecretStrInput(\n name=\"password\",\n display_name=\"OpenSearch Password\",\n value=\"admin\",\n show=False,\n ),\n SecretStrInput(\n name=\"jwt_token\",\n display_name=\"JWT Token\",\n value=\"JWT\",\n load_from_db=False,\n show=True,\n info=(\n \"Valid JSON Web Token for authentication. \"\n \"Will be sent in the Authorization header (with optional 'Bearer ' prefix).\"\n ),\n ),\n StrInput(\n name=\"jwt_header\",\n display_name=\"JWT Header Name\",\n value=\"Authorization\",\n show=False,\n advanced=True,\n ),\n BoolInput(\n name=\"bearer_prefix\",\n display_name=\"Prefix 'Bearer '\",\n value=True,\n show=False,\n advanced=True,\n ),\n # ----- TLS -----\n BoolInput(\n name=\"use_ssl\",\n display_name=\"Use SSL/TLS\",\n value=True,\n advanced=True,\n info=\"Enable SSL/TLS encryption for secure connections to OpenSearch.\",\n ),\n BoolInput(\n name=\"verify_certs\",\n display_name=\"Verify SSL Certificates\",\n value=False,\n advanced=True,\n info=(\n \"Verify SSL certificates when connecting. \"\n \"Disable for self-signed certificates in development environments.\"\n ),\n ),\n ]\n\n # ---------- helper functions for index management ----------\n def _default_text_mapping(\n self,\n dim: int,\n engine: str = \"jvector\",\n space_type: str = \"l2\",\n ef_search: int = 512,\n ef_construction: int = 100,\n m: int = 16,\n vector_field: str = \"vector_field\",\n ) -> dict[str, Any]:\n \"\"\"Create the default OpenSearch index mapping for vector search.\n\n This method generates the index configuration with k-NN settings optimized\n for approximate nearest neighbor search using the specified vector engine.\n\n Args:\n dim: Dimensionality of the vector embeddings\n engine: Vector search engine (jvector, nmslib, faiss, lucene)\n space_type: Distance metric for similarity calculation\n ef_search: Size of dynamic list used during search\n ef_construction: Size of dynamic list used during index construction\n m: Number of bidirectional links for each vector\n vector_field: Name of the field storing vector embeddings\n\n Returns:\n Dictionary containing OpenSearch index mapping configuration\n \"\"\"\n return {\n \"settings\": {\"index\": {\"knn\": True, \"knn.algo_param.ef_search\": ef_search}},\n \"mappings\": {\n \"properties\": {\n vector_field: {\n \"type\": \"knn_vector\",\n \"dimension\": dim,\n \"method\": {\n \"name\": \"disk_ann\",\n \"space_type\": space_type,\n \"engine\": engine,\n \"parameters\": {\"ef_construction\": ef_construction, \"m\": m},\n },\n }\n }\n },\n }\n\n def _validate_aoss_with_engines(self, *, is_aoss: bool, engine: str) -> None:\n \"\"\"Validate engine compatibility with Amazon OpenSearch Serverless (AOSS).\n\n Amazon OpenSearch Serverless has restrictions on which vector engines\n can be used. This method ensures the selected engine is compatible.\n\n Args:\n is_aoss: Whether the connection is to Amazon OpenSearch Serverless\n engine: The selected vector search engine\n\n Raises:\n ValueError: If AOSS is used with an incompatible engine\n \"\"\"\n if is_aoss and engine not in {\"nmslib\", \"faiss\"}:\n msg = \"Amazon OpenSearch Service Serverless only supports `nmslib` or `faiss` engines\"\n raise ValueError(msg)\n\n def _is_aoss_enabled(self, http_auth: Any) -> bool:\n \"\"\"Determine if Amazon OpenSearch Serverless (AOSS) is being used.\n\n Args:\n http_auth: The HTTP authentication object\n\n Returns:\n True if AOSS is enabled, False otherwise\n \"\"\"\n return http_auth is not None and hasattr(http_auth, \"service\") and http_auth.service == \"aoss\"\n\n def _bulk_ingest_embeddings(\n self,\n client: OpenSearch,\n index_name: str,\n embeddings: list[list[float]],\n texts: list[str],\n metadatas: list[dict] | None = None,\n ids: list[str] | None = None,\n vector_field: str = \"vector_field\",\n text_field: str = \"text\",\n mapping: dict | None = None,\n max_chunk_bytes: int | None = 1 * 1024 * 1024,\n *,\n is_aoss: bool = False,\n ) -> list[str]:\n \"\"\"Efficiently ingest multiple documents with embeddings into OpenSearch.\n\n This method uses bulk operations to insert documents with their vector\n embeddings and metadata into the specified OpenSearch index.\n\n Args:\n client: OpenSearch client instance\n index_name: Target index for document storage\n embeddings: List of vector embeddings for each document\n texts: List of document texts\n metadatas: Optional metadata dictionaries for each document\n ids: Optional document IDs (UUIDs generated if not provided)\n vector_field: Field name for storing vector embeddings\n text_field: Field name for storing document text\n mapping: Optional index mapping configuration\n max_chunk_bytes: Maximum size per bulk request chunk\n is_aoss: Whether using Amazon OpenSearch Serverless\n\n Returns:\n List of document IDs that were successfully ingested\n \"\"\"\n if not mapping:\n mapping = {}\n\n requests = []\n return_ids = []\n\n for i, text in enumerate(texts):\n metadata = metadatas[i] if metadatas else {}\n _id = ids[i] if ids else str(uuid.uuid4())\n request = {\n \"_op_type\": \"index\",\n \"_index\": index_name,\n vector_field: embeddings[i],\n text_field: text,\n **metadata,\n }\n if is_aoss:\n request[\"id\"] = _id\n else:\n request[\"_id\"] = _id\n requests.append(request)\n return_ids.append(_id)\n if metadatas:\n self.log(f\"Sample metadata: {metadatas[0] if metadatas else {}}\")\n helpers.bulk(client, requests, max_chunk_bytes=max_chunk_bytes)\n return return_ids\n\n # ---------- auth / client ----------\n def _build_auth_kwargs(self) -> dict[str, Any]:\n \"\"\"Build authentication configuration for OpenSearch client.\n\n Constructs the appropriate authentication parameters based on the\n selected auth mode (basic username/password or JWT token).\n\n Returns:\n Dictionary containing authentication configuration\n\n Raises:\n ValueError: If required authentication parameters are missing\n \"\"\"\n mode = (self.auth_mode or \"basic\").strip().lower()\n if mode == \"jwt\":\n token = (self.jwt_token or \"\").strip()\n if not token:\n msg = \"Auth Mode is 'jwt' but no jwt_token was provided.\"\n raise ValueError(msg)\n header_name = (self.jwt_header or \"Authorization\").strip()\n header_value = f\"Bearer {token}\" if self.bearer_prefix else token\n return {\"headers\": {header_name: header_value}}\n user = (self.username or \"\").strip()\n pwd = (self.password or \"\").strip()\n if not user or not pwd:\n msg = \"Auth Mode is 'basic' but username/password are missing.\"\n raise ValueError(msg)\n return {\"http_auth\": (user, pwd)}\n\n def build_client(self) -> OpenSearch:\n \"\"\"Create and configure an OpenSearch client instance.\n\n Returns:\n Configured OpenSearch client ready for operations\n \"\"\"\n auth_kwargs = self._build_auth_kwargs()\n return OpenSearch(\n hosts=[self.opensearch_url],\n use_ssl=self.use_ssl,\n verify_certs=self.verify_certs,\n ssl_assert_hostname=False,\n ssl_show_warn=False,\n **auth_kwargs,\n )\n\n @check_cached_vector_store\n def build_vector_store(self) -> OpenSearch:\n # Return raw OpenSearch client as our “vector store.”\n self.log(self.ingest_data)\n client = self.build_client()\n self._add_documents_to_vector_store(client=client)\n return client\n\n # ---------- ingest ----------\n def _add_documents_to_vector_store(self, client: OpenSearch) -> None:\n \"\"\"Process and ingest documents into the OpenSearch vector store.\n\n This method handles the complete document ingestion pipeline:\n - Prepares document data and metadata\n - Generates vector embeddings\n - Creates appropriate index mappings\n - Bulk inserts documents with vectors\n\n Args:\n client: OpenSearch client for performing operations\n \"\"\"\n # Convert DataFrame to Data if needed using parent's method\n self.ingest_data = self._prepare_ingest_data()\n\n docs = self.ingest_data or []\n if not docs:\n self.log(\"No documents to ingest.\")\n return\n\n # Extract texts and metadata from documents\n texts = []\n metadatas = []\n # Process docs_metadata table input into a dict\n additional_metadata = {}\n if hasattr(self, \"docs_metadata\") and self.docs_metadata:\n for item in self.docs_metadata:\n if isinstance(item, dict) and \"key\" in item and \"value\" in item:\n additional_metadata[item[\"key\"]] = item[\"value\"]\n\n for doc_obj in docs:\n data_copy = json.loads(doc_obj.model_dump_json())\n text = data_copy.pop(doc_obj.text_key, doc_obj.default_value)\n texts.append(text)\n\n # Merge additional metadata from table input\n data_copy.update(additional_metadata)\n\n metadatas.append(data_copy)\n self.log(metadatas)\n if not self.embedding:\n msg = \"Embedding handle is required to embed documents.\"\n raise ValueError(msg)\n\n # Generate embeddings\n vectors = self.embedding.embed_documents(texts)\n\n if not vectors:\n self.log(\"No vectors generated from documents.\")\n return\n\n # Get vector dimension for mapping\n dim = len(vectors[0]) if vectors else 768 # default fallback\n\n # Check for AOSS\n auth_kwargs = self._build_auth_kwargs()\n is_aoss = self._is_aoss_enabled(auth_kwargs.get(\"http_auth\"))\n\n # Validate engine with AOSS\n engine = getattr(self, \"engine\", \"jvector\")\n self._validate_aoss_with_engines(is_aoss=is_aoss, engine=engine)\n\n # Create mapping with proper KNN settings\n space_type = getattr(self, \"space_type\", \"l2\")\n ef_construction = getattr(self, \"ef_construction\", 512)\n m = getattr(self, \"m\", 16)\n\n mapping = self._default_text_mapping(\n dim=dim,\n engine=engine,\n space_type=space_type,\n ef_construction=ef_construction,\n m=m,\n vector_field=self.vector_field,\n )\n\n self.log(f\"Indexing {len(texts)} documents into '{self.index_name}' with proper KNN mapping...\")\n\n # Use the LangChain-style bulk ingestion\n return_ids = self._bulk_ingest_embeddings(\n client=client,\n index_name=self.index_name,\n embeddings=vectors,\n texts=texts,\n metadatas=metadatas,\n vector_field=self.vector_field,\n text_field=\"text\",\n mapping=mapping,\n is_aoss=is_aoss,\n )\n self.log(metadatas)\n\n self.log(f\"Successfully indexed {len(return_ids)} documents.\")\n\n # ---------- helpers for filters ----------\n def _is_placeholder_term(self, term_obj: dict) -> bool:\n # term_obj like {\"filename\": \"__IMPOSSIBLE_VALUE__\"}\n return any(v == \"__IMPOSSIBLE_VALUE__\" for v in term_obj.values())\n\n def _coerce_filter_clauses(self, filter_obj: dict | None) -> list[dict]:\n \"\"\"Convert filter expressions into OpenSearch-compatible filter clauses.\n\n This method accepts two filter formats and converts them to standardized\n OpenSearch query clauses:\n\n Format A - Explicit filters:\n {\"filter\": [{\"term\": {\"field\": \"value\"}}, {\"terms\": {\"field\": [\"val1\", \"val2\"]}}],\n \"limit\": 10, \"score_threshold\": 1.5}\n\n Format B - Context-style mapping:\n {\"data_sources\": [\"file1.pdf\"], \"document_types\": [\"pdf\"], \"owners\": [\"user1\"]}\n\n Args:\n filter_obj: Filter configuration dictionary or None\n\n Returns:\n List of OpenSearch filter clauses (term/terms objects)\n Placeholder values with \"__IMPOSSIBLE_VALUE__\" are ignored\n \"\"\"\n if not filter_obj:\n return []\n\n # If it is a string, try to parse it once\n if isinstance(filter_obj, str):\n try:\n filter_obj = json.loads(filter_obj)\n except json.JSONDecodeError:\n # Not valid JSON - treat as no filters\n return []\n\n # Case A: already an explicit list/dict under \"filter\"\n if \"filter\" in filter_obj:\n raw = filter_obj[\"filter\"]\n if isinstance(raw, dict):\n raw = [raw]\n explicit_clauses: list[dict] = []\n for f in raw or []:\n if \"term\" in f and isinstance(f[\"term\"], dict) and not self._is_placeholder_term(f[\"term\"]):\n explicit_clauses.append(f)\n elif \"terms\" in f and isinstance(f[\"terms\"], dict):\n field, vals = next(iter(f[\"terms\"].items()))\n if isinstance(vals, list) and len(vals) > 0:\n explicit_clauses.append(f)\n return explicit_clauses\n\n # Case B: convert context-style maps into clauses\n field_mapping = {\n \"data_sources\": \"filename\",\n \"document_types\": \"mimetype\",\n \"owners\": \"owner\",\n }\n context_clauses: list[dict] = []\n for k, values in filter_obj.items():\n if not isinstance(values, list):\n continue\n field = field_mapping.get(k, k)\n if len(values) == 0:\n # Match-nothing placeholder (kept to mirror your tool semantics)\n context_clauses.append({\"term\": {field: \"__IMPOSSIBLE_VALUE__\"}})\n elif len(values) == 1:\n if values[0] != \"__IMPOSSIBLE_VALUE__\":\n context_clauses.append({\"term\": {field: values[0]}})\n else:\n context_clauses.append({\"terms\": {field: values}})\n return context_clauses\n\n # ---------- search (single hybrid path matching your tool) ----------\n def search(self, query: str | None = None) -> list[dict[str, Any]]:\n \"\"\"Perform hybrid search combining vector similarity and keyword matching.\n\n This method executes a sophisticated search that combines:\n - K-nearest neighbor (KNN) vector similarity search (70% weight)\n - Multi-field keyword search with fuzzy matching (30% weight)\n - Optional filtering and score thresholds\n - Aggregations for faceted search results\n\n Args:\n query: Search query string (used for both vector embedding and keyword search)\n\n Returns:\n List of search results with page_content, metadata, and relevance scores\n\n Raises:\n ValueError: If embedding component is not provided or filter JSON is invalid\n \"\"\"\n logger.info(self.ingest_data)\n client = self.build_client()\n q = (query or \"\").strip()\n\n # Parse optional filter expression (can be either A or B shape; see _coerce_filter_clauses)\n filter_obj = None\n if getattr(self, \"filter_expression\", \"\") and self.filter_expression.strip():\n try:\n filter_obj = json.loads(self.filter_expression)\n except json.JSONDecodeError as e:\n msg = f\"Invalid filter_expression JSON: {e}\"\n raise ValueError(msg) from e\n\n if not self.embedding:\n msg = \"Embedding is required to run hybrid search (KNN + keyword).\"\n raise ValueError(msg)\n\n # Embed the query\n vec = self.embedding.embed_query(q)\n\n # Build filter clauses (accept both shapes)\n filter_clauses = self._coerce_filter_clauses(filter_obj)\n\n # Respect the tool's limit/threshold defaults\n limit = (filter_obj or {}).get(\"limit\", self.number_of_results)\n score_threshold = (filter_obj or {}).get(\"score_threshold\", 0)\n\n # Build the same hybrid body as your SearchService\n body = {\n \"query\": {\n \"bool\": {\n \"should\": [\n {\n \"knn\": {\n self.vector_field: {\n \"vector\": vec,\n \"k\": 10, # fixed to match the tool\n \"boost\": 0.7,\n }\n }\n },\n {\n \"multi_match\": {\n \"query\": q,\n \"fields\": [\"text^2\", \"filename^1.5\"],\n \"type\": \"best_fields\",\n \"fuzziness\": \"AUTO\",\n \"boost\": 0.3,\n }\n },\n ],\n \"minimum_should_match\": 1,\n }\n },\n \"aggs\": {\n \"data_sources\": {\"terms\": {\"field\": \"filename\", \"size\": 20}},\n \"document_types\": {\"terms\": {\"field\": \"mimetype\", \"size\": 10}},\n \"owners\": {\"terms\": {\"field\": \"owner\", \"size\": 10}},\n },\n \"_source\": [\n \"filename\",\n \"mimetype\",\n \"page\",\n \"text\",\n \"source_url\",\n \"owner\",\n \"allowed_users\",\n \"allowed_groups\",\n ],\n \"size\": limit,\n }\n if filter_clauses:\n body[\"query\"][\"bool\"][\"filter\"] = filter_clauses\n\n if isinstance(score_threshold, (int, float)) and score_threshold > 0:\n # top-level min_score (matches your tool)\n body[\"min_score\"] = score_threshold\n\n resp = client.search(index=self.index_name, body=body)\n hits = resp.get(\"hits\", {}).get(\"hits\", [])\n return [\n {\n \"page_content\": hit[\"_source\"].get(\"text\", \"\"),\n \"metadata\": {k: v for k, v in hit[\"_source\"].items() if k != \"text\"},\n \"score\": hit.get(\"_score\"),\n }\n for hit in hits\n ]\n\n def search_documents(self) -> list[Data]:\n \"\"\"Search documents and return results as Data objects.\n\n This is the main interface method that performs the search using the\n configured search_query and returns results in Langflow's Data format.\n\n Returns:\n List of Data objects containing search results with text and metadata\n\n Raises:\n Exception: If search operation fails\n \"\"\"\n try:\n raw = self.search(self.search_query or \"\")\n return [Data(text=hit[\"page_content\"], **hit[\"metadata\"]) for hit in raw]\n self.log(self.ingest_data)\n except Exception as e:\n self.log(f\"search_documents error: {e}\")\n raise\n\n # -------- dynamic UI handling (auth switch) --------\n async def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:\n \"\"\"Dynamically update component configuration based on field changes.\n\n This method handles real-time UI updates, particularly for authentication\n mode changes that show/hide relevant input fields.\n\n Args:\n build_config: Current component configuration\n field_value: New value for the changed field\n field_name: Name of the field that changed\n\n Returns:\n Updated build configuration with appropriate field visibility\n \"\"\"\n try:\n if field_name == \"auth_mode\":\n mode = (field_value or \"basic\").strip().lower()\n is_basic = mode == \"basic\"\n is_jwt = mode == \"jwt\"\n\n build_config[\"username\"][\"show\"] = is_basic\n build_config[\"password\"][\"show\"] = is_basic\n\n build_config[\"jwt_token\"][\"show\"] = is_jwt\n build_config[\"jwt_header\"][\"show\"] = is_jwt\n build_config[\"bearer_prefix\"][\"show\"] = is_jwt\n\n build_config[\"username\"][\"required\"] = is_basic\n build_config[\"password\"][\"required\"] = is_basic\n\n build_config[\"jwt_token\"][\"required\"] = is_jwt\n build_config[\"jwt_header\"][\"required\"] = is_jwt\n build_config[\"bearer_prefix\"][\"required\"] = False\n\n if is_basic:\n build_config[\"jwt_token\"][\"value\"] = \"\"\n\n return build_config\n\n except (KeyError, ValueError) as e:\n self.log(f\"update_build_config error: {e}\")\n\n return build_config\n"
|
|
},
|
|
"docs_metadata": {
|
|
"_input_type": "TableInput",
|
|
"advanced": true,
|
|
"display_name": "Document Metadata",
|
|
"dynamic": false,
|
|
"info": "Additional metadata key-value pairs to be added to all ingested documents. Useful for tagging documents with source information, categories, or other custom attributes.",
|
|
"is_list": true,
|
|
"list_add_label": "Add More",
|
|
"name": "docs_metadata",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"table_icon": "Table",
|
|
"table_schema": [
|
|
{
|
|
"description": "Key name",
|
|
"display_name": "Key",
|
|
"name": "key",
|
|
"type": "str"
|
|
},
|
|
{
|
|
"description": "Value of the metadata",
|
|
"display_name": "Value",
|
|
"name": "value",
|
|
"type": "str"
|
|
}
|
|
],
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"trigger_icon": "Table",
|
|
"trigger_text": "Open table",
|
|
"type": "table",
|
|
"value": []
|
|
},
|
|
"ef_construction": {
|
|
"_input_type": "IntInput",
|
|
"advanced": true,
|
|
"display_name": "EF Construction",
|
|
"dynamic": false,
|
|
"info": "Size of the dynamic candidate list during index construction. Higher values improve recall but increase indexing time and memory usage.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "ef_construction",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "int",
|
|
"value": 512
|
|
},
|
|
"embedding": {
|
|
"_input_type": "HandleInput",
|
|
"advanced": false,
|
|
"display_name": "Embedding",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [
|
|
"Embeddings"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "embedding",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"trace_as_metadata": true,
|
|
"type": "other",
|
|
"value": ""
|
|
},
|
|
"engine": {
|
|
"_input_type": "DropdownInput",
|
|
"advanced": true,
|
|
"combobox": false,
|
|
"dialog_inputs": {},
|
|
"display_name": "Vector Engine",
|
|
"dynamic": false,
|
|
"external_options": {},
|
|
"info": "Vector search engine for similarity calculations. 'jvector' is recommended for most use cases. Note: Amazon OpenSearch Serverless only supports 'nmslib' or 'faiss'.",
|
|
"name": "engine",
|
|
"options": [
|
|
"jvector",
|
|
"nmslib",
|
|
"faiss",
|
|
"lucene"
|
|
],
|
|
"options_metadata": [],
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"toggle": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "jvector"
|
|
},
|
|
"filter_expression": {
|
|
"_input_type": "MultilineInput",
|
|
"advanced": false,
|
|
"copy_field": false,
|
|
"display_name": "Search Filters (JSON)",
|
|
"dynamic": false,
|
|
"info": "Optional JSON configuration for search filtering, result limits, and score thresholds.\n\nFormat 1 - Explicit filters:\n{\"filter\": [{\"term\": {\"filename\":\"doc.pdf\"}}, {\"terms\":{\"owner\":[\"user1\",\"user2\"]}}], \"limit\": 10, \"score_threshold\": 1.6}\n\nFormat 2 - Context-style mapping:\n{\"data_sources\":[\"file.pdf\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"user123\"]}\n\nUse __IMPOSSIBLE_VALUE__ as placeholder to ignore specific filters.",
|
|
"input_types": [
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"multiline": true,
|
|
"name": "filter_expression",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"index_name": {
|
|
"_input_type": "StrInput",
|
|
"advanced": false,
|
|
"display_name": "Index Name",
|
|
"dynamic": false,
|
|
"info": "The OpenSearch index name where documents will be stored and searched. Will be created automatically if it doesn't exist.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "index_name",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "documents"
|
|
},
|
|
"ingest_data": {
|
|
"_input_type": "HandleInput",
|
|
"advanced": false,
|
|
"display_name": "Ingest Data",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [
|
|
"Data",
|
|
"DataFrame"
|
|
],
|
|
"list": true,
|
|
"list_add_label": "Add More",
|
|
"name": "ingest_data",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"trace_as_metadata": true,
|
|
"type": "other",
|
|
"value": ""
|
|
},
|
|
"jwt_header": {
|
|
"_input_type": "StrInput",
|
|
"advanced": true,
|
|
"display_name": "JWT Header Name",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "jwt_header",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": false,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "Authorization"
|
|
},
|
|
"jwt_token": {
|
|
"_input_type": "SecretStrInput",
|
|
"advanced": false,
|
|
"display_name": "JWT Token",
|
|
"dynamic": false,
|
|
"info": "Valid JSON Web Token for authentication. Will be sent in the Authorization header (with optional 'Bearer ' prefix).",
|
|
"input_types": [],
|
|
"load_from_db": true,
|
|
"name": "jwt_token",
|
|
"password": true,
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": "JWT"
|
|
},
|
|
"m": {
|
|
"_input_type": "IntInput",
|
|
"advanced": true,
|
|
"display_name": "M Parameter",
|
|
"dynamic": false,
|
|
"info": "Number of bidirectional connections for each vector in the HNSW graph. Higher values improve search quality but increase memory usage and indexing time.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "m",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "int",
|
|
"value": 16
|
|
},
|
|
"number_of_results": {
|
|
"_input_type": "IntInput",
|
|
"advanced": true,
|
|
"display_name": "Default Result Limit",
|
|
"dynamic": false,
|
|
"info": "Default maximum number of search results to return when no limit is specified in the filter expression.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "number_of_results",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "int",
|
|
"value": 4
|
|
},
|
|
"opensearch_url": {
|
|
"_input_type": "StrInput",
|
|
"advanced": false,
|
|
"display_name": "OpenSearch URL",
|
|
"dynamic": false,
|
|
"info": "The connection URL for your OpenSearch cluster (e.g., http://localhost:9200 for local development or your cloud endpoint).",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "opensearch_url",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "https://opensearch:9200"
|
|
},
|
|
"password": {
|
|
"_input_type": "SecretStrInput",
|
|
"advanced": false,
|
|
"display_name": "OpenSearch Password",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [],
|
|
"load_from_db": false,
|
|
"name": "password",
|
|
"password": true,
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": false,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"search_query": {
|
|
"_input_type": "QueryInput",
|
|
"advanced": false,
|
|
"display_name": "Search Query",
|
|
"dynamic": false,
|
|
"info": "Enter a query to run a similarity search.",
|
|
"input_types": [
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "search_query",
|
|
"placeholder": "Enter a query...",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": true,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "query",
|
|
"value": ""
|
|
},
|
|
"should_cache_vector_store": {
|
|
"_input_type": "BoolInput",
|
|
"advanced": true,
|
|
"display_name": "Cache Vector Store",
|
|
"dynamic": false,
|
|
"info": "If True, the vector store will be cached for the current build of the component. This is useful for components that have multiple output methods and want to share the same vector store.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "should_cache_vector_store",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "bool",
|
|
"value": true
|
|
},
|
|
"space_type": {
|
|
"_input_type": "DropdownInput",
|
|
"advanced": true,
|
|
"combobox": false,
|
|
"dialog_inputs": {},
|
|
"display_name": "Distance Metric",
|
|
"dynamic": false,
|
|
"external_options": {},
|
|
"info": "Distance metric for calculating vector similarity. 'l2' (Euclidean) is most common, 'cosinesimil' for cosine similarity, 'innerproduct' for dot product.",
|
|
"name": "space_type",
|
|
"options": [
|
|
"l2",
|
|
"l1",
|
|
"cosinesimil",
|
|
"linf",
|
|
"innerproduct"
|
|
],
|
|
"options_metadata": [],
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"toggle": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "l2"
|
|
},
|
|
"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": {
|
|
"search_query": {
|
|
"default": "",
|
|
"description": "Enter a query to run a similarity search.",
|
|
"title": "Search Query",
|
|
"type": "string"
|
|
}
|
|
},
|
|
"description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
|
|
"display_description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
|
|
"display_name": "search_documents",
|
|
"name": "search_documents",
|
|
"readonly": false,
|
|
"status": true,
|
|
"tags": [
|
|
"search_documents"
|
|
]
|
|
},
|
|
{
|
|
"args": {
|
|
"search_query": {
|
|
"default": "",
|
|
"description": "Enter a query to run a similarity search.",
|
|
"title": "Search Query",
|
|
"type": "string"
|
|
}
|
|
},
|
|
"description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
|
|
"display_description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
|
|
"display_name": "as_dataframe",
|
|
"name": "as_dataframe",
|
|
"readonly": false,
|
|
"status": true,
|
|
"tags": [
|
|
"as_dataframe"
|
|
]
|
|
},
|
|
{
|
|
"args": {
|
|
"search_query": {
|
|
"default": "",
|
|
"description": "Enter a query to run a similarity search.",
|
|
"title": "Search Query",
|
|
"type": "string"
|
|
}
|
|
},
|
|
"description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
|
|
"display_description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
|
|
"display_name": "as_vector_store",
|
|
"name": "as_vector_store",
|
|
"readonly": false,
|
|
"status": true,
|
|
"tags": [
|
|
"as_vector_store"
|
|
]
|
|
}
|
|
]
|
|
},
|
|
"use_ssl": {
|
|
"_input_type": "BoolInput",
|
|
"advanced": true,
|
|
"display_name": "Use SSL/TLS",
|
|
"dynamic": false,
|
|
"info": "Enable SSL/TLS encryption for secure connections to OpenSearch.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "use_ssl",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "bool",
|
|
"value": true
|
|
},
|
|
"username": {
|
|
"_input_type": "StrInput",
|
|
"advanced": false,
|
|
"display_name": "Username",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "username",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": false,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "admin"
|
|
},
|
|
"vector_field": {
|
|
"_input_type": "StrInput",
|
|
"advanced": true,
|
|
"display_name": "Vector Field Name",
|
|
"dynamic": false,
|
|
"info": "Name of the field in OpenSearch documents that stores the vector embeddings for similarity search.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "vector_field",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "chunk_embedding"
|
|
},
|
|
"verify_certs": {
|
|
"_input_type": "BoolInput",
|
|
"advanced": true,
|
|
"display_name": "Verify SSL Certificates",
|
|
"dynamic": false,
|
|
"info": "Verify SSL certificates when connecting. Disable for self-signed certificates in development environments.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "verify_certs",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "bool",
|
|
"value": false
|
|
}
|
|
},
|
|
"tool_mode": true
|
|
},
|
|
"selected_output": "search_results",
|
|
"showNode": true,
|
|
"type": "OpenSearchVectorStoreComponent"
|
|
},
|
|
"dragging": false,
|
|
"id": "OpenSearch-iYfjf",
|
|
"measured": {
|
|
"height": 763,
|
|
"width": 320
|
|
},
|
|
"position": {
|
|
"x": 1202.1762389080463,
|
|
"y": 395.8072555285192
|
|
},
|
|
"selected": false,
|
|
"type": "genericNode"
|
|
},
|
|
{
|
|
"data": {
|
|
"id": "EmbeddingModel-eZ6bT",
|
|
"node": {
|
|
"base_classes": [
|
|
"Embeddings"
|
|
],
|
|
"beta": false,
|
|
"conditional_paths": [],
|
|
"custom_fields": {},
|
|
"description": "Generate embeddings using a specified provider.",
|
|
"display_name": "Embedding Model",
|
|
"documentation": "https://docs.langflow.org/components-embedding-models",
|
|
"edited": false,
|
|
"field_order": [
|
|
"provider",
|
|
"model",
|
|
"api_key",
|
|
"api_base",
|
|
"dimensions",
|
|
"chunk_size",
|
|
"request_timeout",
|
|
"max_retries",
|
|
"show_progress_bar",
|
|
"model_kwargs"
|
|
],
|
|
"frozen": false,
|
|
"icon": "binary",
|
|
"last_updated": "2025-09-26T05:15:05.781Z",
|
|
"legacy": false,
|
|
"lf_version": "1.6.0",
|
|
"metadata": {
|
|
"code_hash": "8607e963fdef",
|
|
"dependencies": {
|
|
"dependencies": [
|
|
{
|
|
"name": "langchain_openai",
|
|
"version": "0.3.23"
|
|
},
|
|
{
|
|
"name": "lfx",
|
|
"version": null
|
|
}
|
|
],
|
|
"total_dependencies": 2
|
|
},
|
|
"module": "custom_components.embedding_model"
|
|
},
|
|
"minimized": false,
|
|
"output_types": [],
|
|
"outputs": [
|
|
{
|
|
"allows_loop": false,
|
|
"cache": true,
|
|
"display_name": "Embedding Model",
|
|
"group_outputs": false,
|
|
"method": "build_embeddings",
|
|
"name": "embeddings",
|
|
"options": null,
|
|
"required_inputs": null,
|
|
"selected": "Embeddings",
|
|
"tool_mode": true,
|
|
"types": [
|
|
"Embeddings"
|
|
],
|
|
"value": "__UNDEFINED__"
|
|
}
|
|
],
|
|
"pinned": false,
|
|
"template": {
|
|
"_type": "Component",
|
|
"api_base": {
|
|
"_input_type": "MessageTextInput",
|
|
"advanced": true,
|
|
"display_name": "API Base URL",
|
|
"dynamic": false,
|
|
"info": "Base URL for the API. Leave empty for default.",
|
|
"input_types": [
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "api_base",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"api_key": {
|
|
"_input_type": "SecretStrInput",
|
|
"advanced": false,
|
|
"display_name": "OpenAI API Key",
|
|
"dynamic": false,
|
|
"info": "Model Provider API key",
|
|
"input_types": [],
|
|
"load_from_db": true,
|
|
"name": "api_key",
|
|
"password": true,
|
|
"placeholder": "",
|
|
"real_time_refresh": true,
|
|
"required": true,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": "OPENAI_API_KEY"
|
|
},
|
|
"chunk_size": {
|
|
"_input_type": "IntInput",
|
|
"advanced": true,
|
|
"display_name": "Chunk Size",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "chunk_size",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "int",
|
|
"value": 1000
|
|
},
|
|
"code": {
|
|
"advanced": true,
|
|
"dynamic": true,
|
|
"fileTypes": [],
|
|
"file_path": "",
|
|
"info": "",
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"multiline": true,
|
|
"name": "code",
|
|
"password": false,
|
|
"placeholder": "",
|
|
"required": true,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "code",
|
|
"value": "from typing import Any\n\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom lfx.base.embeddings.model import LCEmbeddingsModel\nfrom lfx.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageTextInput,\n SecretStrInput,\n)\nfrom lfx.schema.dotdict import dotdict\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n display_name = \"Embedding Model\"\n description = \"Generate embeddings using a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n icon = \"binary\"\n name = \"EmbeddingModel\"\n category = \"models\"\n\n inputs = [\n DropdownInput(\n name=\"provider\",\n display_name=\"Model Provider\",\n options=[\"OpenAI\"],\n value=\"OpenAI\",\n info=\"Select the embedding model provider\",\n real_time_refresh=True,\n options_metadata=[{\"icon\": \"OpenAI\"}],\n ),\n DropdownInput(\n name=\"model\",\n display_name=\"Model Name\",\n options=OPENAI_EMBEDDING_MODEL_NAMES,\n value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n info=\"Select the embedding model to use\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"Model Provider API key\",\n required=True,\n show=True,\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"api_base\",\n display_name=\"API Base URL\",\n info=\"Base URL for the API. Leave empty for default.\",\n advanced=True,\n ),\n IntInput(\n name=\"dimensions\",\n display_name=\"Dimensions\",\n info=\"The number of dimensions the resulting output embeddings should have. \"\n \"Only supported by certain models.\",\n advanced=True,\n ),\n IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n DictInput(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\n ]\n\n def build_embeddings(self) -> Embeddings:\n provider = self.provider\n model = self.model\n api_key = self.api_key\n api_base = self.api_base\n dimensions = self.dimensions\n chunk_size = self.chunk_size\n request_timeout = self.request_timeout\n max_retries = self.max_retries\n show_progress_bar = self.show_progress_bar\n model_kwargs = self.model_kwargs or {}\n\n if provider == \"OpenAI\":\n if not api_key:\n msg = \"OpenAI API key is required when using OpenAI provider\"\n raise ValueError(msg)\n return OpenAIEmbeddings(\n model=model,\n dimensions=dimensions or None,\n base_url=api_base or None,\n api_key=api_key,\n chunk_size=chunk_size,\n max_retries=max_retries,\n timeout=request_timeout or None,\n show_progress_bar=show_progress_bar,\n model_kwargs=model_kwargs,\n )\n msg = f\"Unknown provider: {provider}\"\n raise ValueError(msg)\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n if field_name == \"provider\" and field_value == \"OpenAI\":\n build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n return build_config\n"
|
|
},
|
|
"dimensions": {
|
|
"_input_type": "IntInput",
|
|
"advanced": true,
|
|
"display_name": "Dimensions",
|
|
"dynamic": false,
|
|
"info": "The number of dimensions the resulting output embeddings should have. Only supported by certain models.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "dimensions",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "int",
|
|
"value": ""
|
|
},
|
|
"max_retries": {
|
|
"_input_type": "IntInput",
|
|
"advanced": true,
|
|
"display_name": "Max Retries",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "max_retries",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "int",
|
|
"value": 3
|
|
},
|
|
"model": {
|
|
"_input_type": "DropdownInput",
|
|
"advanced": false,
|
|
"combobox": false,
|
|
"dialog_inputs": {},
|
|
"display_name": "Model Name",
|
|
"dynamic": false,
|
|
"external_options": {},
|
|
"info": "Select the embedding model to use",
|
|
"name": "model",
|
|
"options": [
|
|
"text-embedding-3-small",
|
|
"text-embedding-3-large",
|
|
"text-embedding-ada-002"
|
|
],
|
|
"options_metadata": [],
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
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"title_case": false,
|
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"toggle": false,
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|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "text-embedding-3-small"
|
|
},
|
|
"model_kwargs": {
|
|
"_input_type": "DictInput",
|
|
"advanced": true,
|
|
"display_name": "Model Kwargs",
|
|
"dynamic": false,
|
|
"info": "Additional keyword arguments to pass to the model.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "model_kwargs",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_input": true,
|
|
"type": "dict",
|
|
"value": {}
|
|
},
|
|
"provider": {
|
|
"_input_type": "DropdownInput",
|
|
"advanced": false,
|
|
"combobox": false,
|
|
"dialog_inputs": {},
|
|
"display_name": "Model Provider",
|
|
"dynamic": false,
|
|
"external_options": {},
|
|
"info": "Select the embedding model provider",
|
|
"name": "provider",
|
|
"options": [
|
|
"OpenAI"
|
|
],
|
|
"options_metadata": [
|
|
{
|
|
"icon": "OpenAI"
|
|
}
|
|
],
|
|
"placeholder": "",
|
|
"real_time_refresh": true,
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"toggle": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "OpenAI"
|
|
},
|
|
"request_timeout": {
|
|
"_input_type": "FloatInput",
|
|
"advanced": true,
|
|
"display_name": "Request Timeout",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "request_timeout",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "float",
|
|
"value": ""
|
|
},
|
|
"show_progress_bar": {
|
|
"_input_type": "BoolInput",
|
|
"advanced": true,
|
|
"display_name": "Show Progress Bar",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "show_progress_bar",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "bool",
|
|
"value": false
|
|
}
|
|
},
|
|
"tool_mode": false
|
|
},
|
|
"showNode": true,
|
|
"type": "EmbeddingModel"
|
|
},
|
|
"dragging": false,
|
|
"id": "EmbeddingModel-eZ6bT",
|
|
"measured": {
|
|
"height": 369,
|
|
"width": 320
|
|
},
|
|
"position": {
|
|
"x": 727.4791597769406,
|
|
"y": 518.0820551650631
|
|
},
|
|
"selected": false,
|
|
"type": "genericNode"
|
|
},
|
|
{
|
|
"data": {
|
|
"description": "Define the agent's instructions, then enter a task to complete using tools.",
|
|
"display_name": "Agent",
|
|
"id": "Agent-crjWf",
|
|
"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": [
|
|
"agent_llm",
|
|
"max_tokens",
|
|
"model_kwargs",
|
|
"model_name",
|
|
"openai_api_base",
|
|
"api_key",
|
|
"temperature",
|
|
"seed",
|
|
"max_retries",
|
|
"timeout",
|
|
"system_prompt",
|
|
"n_messages",
|
|
"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": "2025-09-26T05:15:05.782Z",
|
|
"legacy": false,
|
|
"lf_version": "1.6.0",
|
|
"metadata": {
|
|
"code_hash": "ccf23ef1345b",
|
|
"dependencies": {
|
|
"dependencies": [
|
|
{
|
|
"name": "langchain_core",
|
|
"version": "0.3.76"
|
|
},
|
|
{
|
|
"name": "pydantic",
|
|
"version": "2.10.6"
|
|
},
|
|
{
|
|
"name": "lfx",
|
|
"version": null
|
|
}
|
|
],
|
|
"total_dependencies": 3
|
|
},
|
|
"module": "custom_components.agent"
|
|
},
|
|
"minimized": false,
|
|
"output_types": [],
|
|
"outputs": [
|
|
{
|
|
"allows_loop": false,
|
|
"cache": true,
|
|
"display_name": "Response",
|
|
"group_outputs": false,
|
|
"method": "message_response",
|
|
"name": "response",
|
|
"options": null,
|
|
"required_inputs": null,
|
|
"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",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "bool",
|
|
"value": true
|
|
},
|
|
"agent_description": {
|
|
"_input_type": "MultilineInput",
|
|
"advanced": true,
|
|
"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",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "A helpful assistant with access to the following tools:"
|
|
},
|
|
"agent_llm": {
|
|
"_input_type": "DropdownInput",
|
|
"advanced": false,
|
|
"combobox": false,
|
|
"dialog_inputs": {},
|
|
"display_name": "Model Provider",
|
|
"dynamic": false,
|
|
"external_options": {
|
|
"fields": {
|
|
"data": {
|
|
"node": {
|
|
"display_name": "Connect other models",
|
|
"icon": "CornerDownLeft",
|
|
"name": "connect_other_models"
|
|
}
|
|
}
|
|
}
|
|
},
|
|
"info": "The provider of the language model that the agent will use to generate responses.",
|
|
"input_types": [],
|
|
"load_from_db": false,
|
|
"name": "agent_llm",
|
|
"options": [
|
|
"Anthropic",
|
|
"Google Generative AI",
|
|
"OpenAI"
|
|
],
|
|
"options_metadata": [
|
|
{
|
|
"icon": "Anthropic"
|
|
},
|
|
{
|
|
"icon": "GoogleGenerativeAI"
|
|
},
|
|
{
|
|
"icon": "OpenAI"
|
|
}
|
|
],
|
|
"placeholder": "",
|
|
"real_time_refresh": true,
|
|
"refresh_button": false,
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"toggle": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "OpenAI"
|
|
},
|
|
"api_key": {
|
|
"_input_type": "SecretStrInput",
|
|
"advanced": false,
|
|
"display_name": "OpenAI API Key",
|
|
"dynamic": false,
|
|
"info": "The OpenAI API Key to use for the OpenAI model.",
|
|
"input_types": [],
|
|
"load_from_db": true,
|
|
"name": "api_key",
|
|
"password": true,
|
|
"placeholder": "",
|
|
"real_time_refresh": true,
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": "OPENAI_API_KEY"
|
|
},
|
|
"code": {
|
|
"advanced": true,
|
|
"dynamic": true,
|
|
"fileTypes": [],
|
|
"file_path": "",
|
|
"info": "",
|
|
"input_types": [],
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"multiline": true,
|
|
"name": "code",
|
|
"password": false,
|
|
"placeholder": "",
|
|
"required": true,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "code",
|
|
"value": "import json\nimport re\n\nfrom langchain_core.tools import StructuredTool, Tool\nfrom pydantic import ValidationError\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.models.model_input_constants import (\n ALL_PROVIDER_FIELDS,\n MODEL_DYNAMIC_UPDATE_FIELDS,\n MODEL_PROVIDERS_DICT,\n MODEL_PROVIDERS_LIST,\n MODELS_METADATA,\n)\nfrom lfx.base.models.model_utils import get_model_name\nfrom lfx.components.helpers.current_date import CurrentDateComponent\nfrom lfx.components.helpers.memory import MemoryComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.custom.utils import update_component_build_config\nfrom lfx.helpers.base_model import build_model_from_schema\nfrom lfx.inputs.inputs import BoolInput\nfrom lfx.io import DropdownInput, IntInput, MultilineInput, Output, 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 # Filter out json_mode from OpenAI inputs since we handle structured output differently\n if \"OpenAI\" in MODEL_PROVIDERS_DICT:\n openai_inputs_filtered = [\n input_field\n for input_field in MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"]\n if not (hasattr(input_field, \"name\") and input_field.name == \"json_mode\")\n ]\n else:\n openai_inputs_filtered = []\n\n inputs = [\n DropdownInput(\n name=\"agent_llm\",\n display_name=\"Model Provider\",\n info=\"The provider of the language model that the agent will use to generate responses.\",\n options=[*MODEL_PROVIDERS_LIST],\n value=\"OpenAI\",\n real_time_refresh=True,\n refresh_button=False,\n input_types=[],\n options_metadata=[MODELS_METADATA[key] for key in MODEL_PROVIDERS_LIST if key in MODELS_METADATA],\n external_options={\n \"fields\": {\n \"data\": {\n \"node\": {\n \"name\": \"connect_other_models\",\n \"display_name\": \"Connect other models\",\n \"icon\": \"CornerDownLeft\",\n }\n }\n },\n },\n ),\n *openai_inputs_filtered,\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 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 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 async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n llm_model, display_name = await 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 self.model_name = get_model_name(llm_model, display_name=display_name)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\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 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 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 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 async def get_llm(self):\n if not isinstance(self.agent_llm, str):\n return self.agent_llm, None\n\n try:\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if not provider_info:\n msg = f\"Invalid model provider: {self.agent_llm}\"\n raise ValueError(msg)\n\n component_class = provider_info.get(\"component_class\")\n display_name = component_class.display_name\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\", \"\")\n\n return self._build_llm_model(component_class, inputs, prefix), display_name\n\n except (AttributeError, ValueError, TypeError, RuntimeError) as e:\n await logger.aerror(f\"Error building {self.agent_llm} language model: {e!s}\")\n msg = f\"Failed to initialize language model: {e!s}\"\n raise ValueError(msg) from e\n\n def _build_llm_model(self, component, inputs, prefix=\"\"):\n model_kwargs = {}\n for input_ in inputs:\n if hasattr(self, f\"{prefix}{input_.name}\"):\n model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n return component.set(**model_kwargs).build_model()\n\n def set_component_params(self, component):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\")\n # Filter out json_mode and only use attributes that exist on this component\n model_kwargs = {}\n for input_ in inputs:\n if hasattr(self, f\"{prefix}{input_.name}\"):\n model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n\n return component.set(**model_kwargs)\n return component\n\n def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n \"\"\"Delete specified fields from build_config.\"\"\"\n for field in fields:\n build_config.pop(field, None)\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self, build_config: dotdict, field_value: str, field_name: str | None = None\n ) -> dotdict:\n # Iterate over all providers in the MODEL_PROVIDERS_DICT\n # Existing logic for updating build_config\n if field_name in (\"agent_llm\",):\n build_config[\"agent_llm\"][\"value\"] = field_value\n provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n # Call the component class's update_build_config method\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n\n provider_configs: dict[str, tuple[dict, list[dict]]] = {\n provider: (\n MODEL_PROVIDERS_DICT[provider][\"fields\"],\n [\n MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n for other_provider in MODEL_PROVIDERS_DICT\n if other_provider != provider\n ],\n )\n for provider in MODEL_PROVIDERS_DICT\n }\n if field_value in provider_configs:\n fields_to_add, fields_to_delete = provider_configs[field_value]\n\n # Delete fields from other providers\n for fields in fields_to_delete:\n self.delete_fields(build_config, fields)\n\n # Add provider-specific fields\n if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n build_config.update(fields_to_add)\n else:\n build_config.update(fields_to_add)\n # Reset input types for agent_llm\n build_config[\"agent_llm\"][\"input_types\"] = []\n build_config[\"agent_llm\"][\"display_name\"] = \"Model Provider\"\n elif field_value == \"connect_other_models\":\n # Delete all provider fields\n self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n # # Update with custom component\n custom_component = DropdownInput(\n name=\"agent_llm\",\n display_name=\"Language Model\",\n info=\"The provider of the language model that the agent will use to generate responses.\",\n options=[*MODEL_PROVIDERS_LIST],\n real_time_refresh=True,\n refresh_button=False,\n input_types=[\"LanguageModel\"],\n placeholder=\"Awaiting model input.\",\n options_metadata=[MODELS_METADATA[key] for key in MODEL_PROVIDERS_LIST if key in MODELS_METADATA],\n external_options={\n \"fields\": {\n \"data\": {\n \"node\": {\n \"name\": \"connect_other_models\",\n \"display_name\": \"Connect other models\",\n \"icon\": \"CornerDownLeft\",\n },\n }\n },\n },\n )\n build_config.update({\"agent_llm\": custom_component.to_dict()})\n # Update input types for all fields\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"agent_llm\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n if (\n isinstance(self.agent_llm, str)\n and self.agent_llm in MODEL_PROVIDERS_DICT\n and field_name in MODEL_DYNAMIC_UPDATE_FIELDS\n ):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n component_class = self.set_component_params(component_class)\n prefix = provider_info.get(\"prefix\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n # Call each component class's update_build_config method\n # remove the prefix from the field_name\n if isinstance(field_name, str) and isinstance(prefix, str):\n field_name = field_name.replace(prefix, \"\")\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\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 tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\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 return tools\n"
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},
|
|
"format_instructions": {
|
|
"_input_type": "MultilineInput",
|
|
"advanced": true,
|
|
"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",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"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",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": 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",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": true,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"json_mode": {
|
|
"_input_type": "BoolInput",
|
|
"advanced": true,
|
|
"display_name": "JSON Mode",
|
|
"dynamic": false,
|
|
"info": "If True, it will output JSON regardless of passing a schema.",
|
|
"input_types": [],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "json_mode",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "bool",
|
|
"value": false
|
|
},
|
|
"max_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",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "int",
|
|
"value": 15
|
|
},
|
|
"max_retries": {
|
|
"_input_type": "IntInput",
|
|
"advanced": true,
|
|
"display_name": "Max Retries",
|
|
"dynamic": false,
|
|
"info": "The maximum number of retries to make when generating.",
|
|
"input_types": [],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "max_retries",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "int",
|
|
"value": 5
|
|
},
|
|
"max_tokens": {
|
|
"_input_type": "IntInput",
|
|
"advanced": true,
|
|
"display_name": "Max Tokens",
|
|
"dynamic": false,
|
|
"info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
|
|
"input_types": [],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "max_tokens",
|
|
"placeholder": "",
|
|
"range_spec": {
|
|
"max": 128000,
|
|
"min": 0,
|
|
"step": 0.1,
|
|
"step_type": "float"
|
|
},
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "int",
|
|
"value": ""
|
|
},
|
|
"model_kwargs": {
|
|
"_input_type": "DictInput",
|
|
"advanced": true,
|
|
"display_name": "Model Kwargs",
|
|
"dynamic": false,
|
|
"info": "Additional keyword arguments to pass to the model.",
|
|
"input_types": [],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "model_kwargs",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_input": true,
|
|
"type": "dict",
|
|
"value": {}
|
|
},
|
|
"model_name": {
|
|
"_input_type": "DropdownInput",
|
|
"advanced": false,
|
|
"combobox": true,
|
|
"dialog_inputs": {},
|
|
"display_name": "Model Name",
|
|
"dynamic": false,
|
|
"external_options": {},
|
|
"info": "To see the model names, first choose a provider. Then, enter your API key and click the refresh button next to the model name.",
|
|
"input_types": [],
|
|
"name": "model_name",
|
|
"options": [
|
|
"gpt-4o-mini",
|
|
"gpt-4o",
|
|
"gpt-4.1",
|
|
"gpt-4.1-mini",
|
|
"gpt-4.1-nano",
|
|
"gpt-4-turbo",
|
|
"gpt-4-turbo-preview",
|
|
"gpt-4",
|
|
"gpt-3.5-turbo",
|
|
"gpt-5",
|
|
"gpt-5-mini",
|
|
"gpt-5-nano",
|
|
"gpt-5-chat-latest",
|
|
"o1",
|
|
"o3-mini",
|
|
"o3",
|
|
"o3-pro",
|
|
"o4-mini",
|
|
"o4-mini-high"
|
|
],
|
|
"options_metadata": [],
|
|
"placeholder": "",
|
|
"real_time_refresh": false,
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"toggle": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "gpt-4.1"
|
|
},
|
|
"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",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "int",
|
|
"value": 100
|
|
},
|
|
"openai_api_base": {
|
|
"_input_type": "StrInput",
|
|
"advanced": true,
|
|
"display_name": "OpenAI API Base",
|
|
"dynamic": false,
|
|
"info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
|
|
"input_types": [],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "openai_api_base",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"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": [],
|
|
"is_list": true,
|
|
"list_add_label": "Add More",
|
|
"name": "output_schema",
|
|
"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,
|
|
"trigger_icon": "Table",
|
|
"trigger_text": "Open table",
|
|
"type": "table",
|
|
"value": []
|
|
},
|
|
"seed": {
|
|
"_input_type": "IntInput",
|
|
"advanced": true,
|
|
"display_name": "Seed",
|
|
"dynamic": false,
|
|
"info": "The seed controls the reproducibility of the job.",
|
|
"input_types": [],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "seed",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "int",
|
|
"value": 1
|
|
},
|
|
"system_prompt": {
|
|
"_input_type": "MultilineInput",
|
|
"advanced": 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",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "You are a helpful assistant that can use tools to answer questions and perform tasks."
|
|
},
|
|
"temperature": {
|
|
"_input_type": "SliderInput",
|
|
"advanced": true,
|
|
"display_name": "Temperature",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [],
|
|
"max_label": "",
|
|
"max_label_icon": "",
|
|
"min_label": "",
|
|
"min_label_icon": "",
|
|
"name": "temperature",
|
|
"placeholder": "",
|
|
"range_spec": {
|
|
"max": 1,
|
|
"min": 0,
|
|
"step": 0.01,
|
|
"step_type": "float"
|
|
},
|
|
"required": false,
|
|
"show": true,
|
|
"slider_buttons": false,
|
|
"slider_buttons_options": [],
|
|
"slider_input": false,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"type": "slider",
|
|
"value": 0.1
|
|
},
|
|
"timeout": {
|
|
"_input_type": "IntInput",
|
|
"advanced": true,
|
|
"display_name": "Timeout",
|
|
"dynamic": false,
|
|
"info": "The timeout for requests to OpenAI completion API.",
|
|
"input_types": [],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "timeout",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "int",
|
|
"value": 700
|
|
},
|
|
"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",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"trace_as_metadata": true,
|
|
"type": "other",
|
|
"value": ""
|
|
},
|
|
"verbose": {
|
|
"_input_type": "BoolInput",
|
|
"advanced": true,
|
|
"display_name": "Verbose",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "verbose",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "bool",
|
|
"value": true
|
|
}
|
|
},
|
|
"tool_mode": false
|
|
},
|
|
"selected_output": "response",
|
|
"showNode": true,
|
|
"type": "Agent"
|
|
},
|
|
"dragging": false,
|
|
"id": "Agent-crjWf",
|
|
"measured": {
|
|
"height": 594,
|
|
"width": 320
|
|
},
|
|
"position": {
|
|
"x": 1686.5732118555798,
|
|
"y": 317.94354236557473
|
|
},
|
|
"selected": false,
|
|
"type": "genericNode"
|
|
},
|
|
{
|
|
"data": {
|
|
"id": "TextInput-aHsQb",
|
|
"node": {
|
|
"base_classes": [
|
|
"Message"
|
|
],
|
|
"beta": false,
|
|
"conditional_paths": [],
|
|
"custom_fields": {},
|
|
"description": "Get user text inputs.",
|
|
"display_name": "Text Input",
|
|
"documentation": "https://docs.langflow.org/components-io#text-input",
|
|
"edited": true,
|
|
"field_order": [
|
|
"input_value"
|
|
],
|
|
"frozen": false,
|
|
"icon": "type",
|
|
"legacy": false,
|
|
"lf_version": "1.6.0",
|
|
"metadata": {},
|
|
"minimized": false,
|
|
"output_types": [],
|
|
"outputs": [
|
|
{
|
|
"allows_loop": false,
|
|
"cache": true,
|
|
"display_name": "Output Text",
|
|
"group_outputs": false,
|
|
"hidden": null,
|
|
"method": "text_response",
|
|
"name": "text",
|
|
"options": null,
|
|
"required_inputs": null,
|
|
"selected": "Message",
|
|
"tool_mode": true,
|
|
"types": [
|
|
"Message"
|
|
],
|
|
"value": "__UNDEFINED__"
|
|
}
|
|
],
|
|
"pinned": false,
|
|
"template": {
|
|
"_type": "Component",
|
|
"code": {
|
|
"advanced": true,
|
|
"dynamic": true,
|
|
"fileTypes": [],
|
|
"file_path": "",
|
|
"info": "",
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"multiline": true,
|
|
"name": "code",
|
|
"password": false,
|
|
"placeholder": "",
|
|
"required": true,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "code",
|
|
"value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import SecretStrInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n display_name = \"Text Input\"\n description = \"Get user text inputs.\"\n documentation: str = \"https://docs.langflow.org/components-io#text-input\"\n icon = \"type\"\n name = \"TextInput\"\n\n inputs = [\n SecretStrInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Text to be passed as input.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Output Text\", name=\"text\", method=\"text_response\"),\n ]\n\n def text_response(self) -> Message:\n return Message(\n text=self.input_value,\n )\n"
|
|
},
|
|
"input_value": {
|
|
"_input_type": "SecretStrInput",
|
|
"advanced": false,
|
|
"display_name": "Text",
|
|
"dynamic": false,
|
|
"info": "Text to be passed as input.",
|
|
"input_types": [],
|
|
"load_from_db": true,
|
|
"name": "input_value",
|
|
"password": true,
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": "OPENRAG-QUERY-FILTER"
|
|
}
|
|
},
|
|
"tool_mode": false
|
|
},
|
|
"showNode": true,
|
|
"type": "TextInput"
|
|
},
|
|
"dragging": false,
|
|
"id": "TextInput-aHsQb",
|
|
"measured": {
|
|
"height": 204,
|
|
"width": 320
|
|
},
|
|
"position": {
|
|
"x": 745.3341059713564,
|
|
"y": 95.0152511387621
|
|
},
|
|
"selected": false,
|
|
"type": "genericNode"
|
|
},
|
|
{
|
|
"data": {
|
|
"id": "LanguageModelComponent-0YME7",
|
|
"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": [
|
|
"provider",
|
|
"model_name",
|
|
"api_key",
|
|
"input_value",
|
|
"system_message",
|
|
"stream",
|
|
"temperature"
|
|
],
|
|
"frozen": false,
|
|
"icon": "brain-circuit",
|
|
"last_updated": "2025-09-26T05:15:05.784Z",
|
|
"legacy": false,
|
|
"metadata": {
|
|
"code_hash": "bb5f8714781b",
|
|
"dependencies": {
|
|
"dependencies": [
|
|
{
|
|
"name": "langchain_anthropic",
|
|
"version": "0.3.14"
|
|
},
|
|
{
|
|
"name": "langchain_google_genai",
|
|
"version": "2.0.6"
|
|
},
|
|
{
|
|
"name": "langchain_openai",
|
|
"version": "0.3.23"
|
|
},
|
|
{
|
|
"name": "lfx",
|
|
"version": null
|
|
}
|
|
],
|
|
"total_dependencies": 4
|
|
},
|
|
"keywords": [
|
|
"model",
|
|
"llm",
|
|
"language model",
|
|
"large language model"
|
|
],
|
|
"module": "custom_components.language_model"
|
|
},
|
|
"minimized": false,
|
|
"output_types": [],
|
|
"outputs": [
|
|
{
|
|
"allows_loop": false,
|
|
"cache": true,
|
|
"display_name": "Model Response",
|
|
"group_outputs": false,
|
|
"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,
|
|
"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": {
|
|
"_type": "Component",
|
|
"api_key": {
|
|
"_input_type": "SecretStrInput",
|
|
"advanced": false,
|
|
"display_name": "OpenAI API Key",
|
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"dynamic": false,
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"info": "Model Provider API key",
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"input_types": [],
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"load_from_db": true,
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"name": "api_key",
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"password": true,
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"placeholder": "",
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"real_time_refresh": true,
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"required": false,
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"show": true,
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"title_case": false,
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"type": "str",
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"value": "OPENAI_API_KEY"
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},
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"code": {
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"advanced": true,
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"dynamic": true,
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"fileTypes": [],
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"file_path": "",
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"info": "",
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"list": false,
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"load_from_db": false,
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"multiline": true,
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"name": "code",
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"password": false,
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"placeholder": "",
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"required": true,
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"show": true,
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"title_case": false,
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"type": "code",
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"value": "from typing import Any\n\nfrom langchain_anthropic import ChatAnthropic\nfrom langchain_google_genai import ChatGoogleGenerativeAI\nfrom langchain_openai import ChatOpenAI\n\nfrom lfx.base.models.anthropic_constants import ANTHROPIC_MODELS\nfrom lfx.base.models.google_generative_ai_constants import GOOGLE_GENERATIVE_AI_MODELS\nfrom lfx.base.models.model import LCModelComponent\nfrom lfx.base.models.openai_constants import OPENAI_CHAT_MODEL_NAMES, OPENAI_REASONING_MODEL_NAMES\nfrom lfx.field_typing import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput\nfrom lfx.io import DropdownInput, MessageInput, MultilineInput, SecretStrInput, SliderInput\nfrom lfx.schema.dotdict import dotdict\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 priority = 0 # Set priority to 0 to make it appear first\n\n inputs = [\n DropdownInput(\n name=\"provider\",\n display_name=\"Model Provider\",\n options=[\"OpenAI\", \"Anthropic\", \"Google\"],\n value=\"OpenAI\",\n info=\"Select the model provider\",\n real_time_refresh=True,\n options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Anthropic\"}, {\"icon\": \"GoogleGenerativeAI\"}],\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n options=OPENAI_CHAT_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES,\n value=OPENAI_CHAT_MODEL_NAMES[0],\n info=\"Select the model to use\",\n real_time_refresh=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"Model Provider API key\",\n required=False,\n show=True,\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 ]\n\n def build_model(self) -> LanguageModel:\n provider = self.provider\n model_name = self.model_name\n temperature = self.temperature\n stream = self.stream\n\n if provider == \"OpenAI\":\n if not self.api_key:\n msg = \"OpenAI API key is required when using OpenAI provider\"\n raise ValueError(msg)\n\n if model_name in OPENAI_REASONING_MODEL_NAMES:\n # reasoning models do not support temperature (yet)\n temperature = None\n\n return ChatOpenAI(\n model_name=model_name,\n temperature=temperature,\n streaming=stream,\n openai_api_key=self.api_key,\n )\n if provider == \"Anthropic\":\n if not self.api_key:\n msg = \"Anthropic API key is required when using Anthropic provider\"\n raise ValueError(msg)\n return ChatAnthropic(\n model=model_name,\n temperature=temperature,\n streaming=stream,\n anthropic_api_key=self.api_key,\n )\n if provider == \"Google\":\n if not self.api_key:\n msg = \"Google API key is required when using Google provider\"\n raise ValueError(msg)\n return ChatGoogleGenerativeAI(\n model=model_name,\n temperature=temperature,\n streaming=stream,\n google_api_key=self.api_key,\n )\n msg = f\"Unknown provider: {provider}\"\n raise ValueError(msg)\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n if field_name == \"provider\":\n if field_value == \"OpenAI\":\n build_config[\"model_name\"][\"options\"] = OPENAI_CHAT_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES\n build_config[\"model_name\"][\"value\"] = OPENAI_CHAT_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n elif field_value == \"Anthropic\":\n build_config[\"model_name\"][\"options\"] = ANTHROPIC_MODELS\n build_config[\"model_name\"][\"value\"] = ANTHROPIC_MODELS[0]\n build_config[\"api_key\"][\"display_name\"] = \"Anthropic API Key\"\n elif field_value == \"Google\":\n build_config[\"model_name\"][\"options\"] = GOOGLE_GENERATIVE_AI_MODELS\n build_config[\"model_name\"][\"value\"] = GOOGLE_GENERATIVE_AI_MODELS[0]\n build_config[\"api_key\"][\"display_name\"] = \"Google API Key\"\n elif field_name == \"model_name\" and field_value.startswith(\"o1\") and self.provider == \"OpenAI\":\n # Hide system_message for o1 models - currently unsupported\n if \"system_message\" in build_config:\n build_config[\"system_message\"][\"show\"] = False\n elif field_name == \"model_name\" and not field_value.startswith(\"o1\") and \"system_message\" in build_config:\n build_config[\"system_message\"][\"show\"] = True\n return build_config\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,
|
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"name": "input_value",
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|
"placeholder": "",
|
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"required": false,
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"show": true,
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"title_case": false,
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"tool_mode": false,
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"trace_as_input": true,
|
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"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"model_name": {
|
|
"_input_type": "DropdownInput",
|
|
"advanced": false,
|
|
"combobox": false,
|
|
"dialog_inputs": {},
|
|
"display_name": "Model Name",
|
|
"dynamic": false,
|
|
"external_options": {},
|
|
"info": "Select the model to use",
|
|
"name": "model_name",
|
|
"options": [
|
|
"gpt-4o-mini",
|
|
"gpt-4o",
|
|
"gpt-4.1",
|
|
"gpt-4.1-mini",
|
|
"gpt-4.1-nano",
|
|
"gpt-4-turbo",
|
|
"gpt-4-turbo-preview",
|
|
"gpt-4",
|
|
"gpt-3.5-turbo",
|
|
"gpt-5",
|
|
"gpt-5-mini",
|
|
"gpt-5-nano",
|
|
"gpt-5-chat-latest",
|
|
"o1",
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|
"o3-mini",
|
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"o3",
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"o3-pro",
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"o4-mini",
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"o4-mini-high"
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],
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"options_metadata": [],
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"placeholder": "",
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"real_time_refresh": true,
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"required": false,
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"show": true,
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"title_case": false,
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"toggle": false,
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"tool_mode": false,
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"trace_as_metadata": true,
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"type": "str",
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"value": "gpt-4o-mini"
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},
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"provider": {
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"_input_type": "DropdownInput",
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"advanced": false,
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"combobox": false,
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"dialog_inputs": {},
|
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"display_name": "Model Provider",
|
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"dynamic": false,
|
|
"external_options": {},
|
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"info": "Select the model provider",
|
|
"name": "provider",
|
|
"options": [
|
|
"OpenAI",
|
|
"Anthropic",
|
|
"Google"
|
|
],
|
|
"options_metadata": [
|
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{
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"icon": "OpenAI"
|
|
},
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{
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"icon": "Anthropic"
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},
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{
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"icon": "GoogleGenerativeAI"
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}
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],
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"placeholder": "",
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"real_time_refresh": true,
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"required": false,
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"show": true,
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"title_case": false,
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"toggle": false,
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"tool_mode": false,
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"trace_as_metadata": true,
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"type": "str",
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"value": "OpenAI"
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},
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"stream": {
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"_input_type": "BoolInput",
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"advanced": true,
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"display_name": "Stream",
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"dynamic": false,
|
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"info": "Whether to stream the response",
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"list": false,
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"list_add_label": "Add More",
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"name": "stream",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"tool_mode": false,
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"trace_as_metadata": true,
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"type": "bool",
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"value": false
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},
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"system_message": {
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"_input_type": "MultilineInput",
|
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"advanced": false,
|
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"copy_field": false,
|
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"display_name": "System Message",
|
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"dynamic": false,
|
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"info": "A system message that helps set the behavior of the assistant",
|
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"input_types": [
|
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"Message"
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],
|
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"list": false,
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"list_add_label": "Add More",
|
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"load_from_db": false,
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"multiline": true,
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"name": "system_message",
|
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"placeholder": "",
|
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"required": false,
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"show": true,
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"title_case": false,
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"tool_mode": false,
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"trace_as_input": true,
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"trace_as_metadata": true,
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"type": "str",
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"value": ""
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},
|
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"temperature": {
|
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"_input_type": "SliderInput",
|
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"advanced": true,
|
|
"display_name": "Temperature",
|
|
"dynamic": false,
|
|
"info": "Controls randomness in responses",
|
|
"max_label": "",
|
|
"max_label_icon": "",
|
|
"min_label": "",
|
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"min_label_icon": "",
|
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"name": "temperature",
|
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"placeholder": "",
|
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"range_spec": {
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"max": 1,
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"min": 0,
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"step": 0.01,
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"step_type": "float"
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},
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"required": false,
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"slider_buttons": false,
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"slider_buttons_options": [],
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"slider_input": false,
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"title_case": false,
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"tool_mode": false,
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"type": "slider",
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"value": 0.1
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}
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},
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"tool_mode": false
|
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},
|
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"selected_output": "model_output",
|
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"showNode": true,
|
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"type": "LanguageModelComponent"
|
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},
|
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"dragging": false,
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"id": "LanguageModelComponent-0YME7",
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"measured": {
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"height": 534,
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"width": 320
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},
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"position": {
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"x": 1206.0291133693556,
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"y": -185.39565741253472
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},
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"selected": false,
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"type": "genericNode"
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}
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],
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"viewport": {
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"x": -234.8770309457758,
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"y": 153.7254076573895,
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"zoom": 0.6026322796158203
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}
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},
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"description": "OpenRAG Open Search Agent",
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"endpoint_name": null,
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"id": "1098eea1-6649-4e1d-aed1-b77249fb8dd0",
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"is_component": false,
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"last_tested_version": "1.6.0",
|
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"name": "OpenRAG Open Search Agent",
|
|
"tags": [
|
|
"assistants",
|
|
"agents"
|
|
]
|
|
} |