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chore(templates): add user interaction agent (#11185)
### What problem does this PR solve? Add user interaction agent template ### Type of change - [x] Other (please describe): new agent template
This commit is contained in:
LeonTung
519
agent/templates/user_interaction.json
Normal file
519
agent/templates/user_interaction.json
Normal file
@ -0,0 +1,519 @@
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{
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"id": 27,
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"title": {
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"en": "Interacting with the Agent",
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"zh": "用户与 Agent 交互"
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},
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"description": {
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"en": "During the Agent’s execution, users can actively intervene and interact with the Agent to adjust or guide its output, ensuring the final result aligns with their intentions.",
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"zh": "在 Agent 的运行过程中，用户可以随时介入，与 Agent 进行交互，以调整或引导生成结果，使最终输出更符合预期。"
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},
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"canvas_type": "Agent",
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"dsl": {
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"components": {
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"Agent:LargeFliesMelt": {
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"downstream": [
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"UserFillUp:GoldBroomsRelate"
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],
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"obj": {
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||||
"component_name": "Agent",
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||||
"params": {
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||||
"cite": true,
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||||
"delay_after_error": 1,
|
||||
"description": "",
|
||||
"exception_default_value": "",
|
||||
"exception_goto": [],
|
||||
"exception_method": "",
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||||
"frequencyPenaltyEnabled": false,
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||||
"frequency_penalty": 0.7,
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||||
"llm_id": "qwen-turbo@Tongyi-Qianwen",
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||||
"maxTokensEnabled": false,
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"max_retries": 3,
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"max_rounds": 1,
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"max_tokens": 256,
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"mcp": [],
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||||
"message_history_window_size": 12,
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"outputs": {
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"content": {
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||||
"type": "string",
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||||
"value": ""
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||||
},
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||||
"structured": {}
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||||
},
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"presencePenaltyEnabled": false,
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"presence_penalty": 0.4,
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"prompts": [
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{
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"content": "User query:{sys.query}",
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"role": "user"
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}
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],
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"sys_prompt": "<role>\nYou are the Planning Agent in a multi-agent RAG workflow.\nYour sole job is to design a crisp, executable Search Plan for the next agent. Do not search or answer the user’s question.\n</role>\n<objectives>\nUnderstand the user’s task and decompose it into evidence-seeking steps.\nProduce high-quality queries and retrieval settings tailored to the task type (fact lookup, multi-hop reasoning, comparison, statistics, how-to, etc.).\nIdentify missing information that would materially change the plan (≤3 concise questions).\nOptimize for source trustworthiness, diversity, and recency; define stopping criteria to avoid over-searching.\nAnswer in 150 words.\n<objectives>",
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"temperature": 0.1,
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"temperatureEnabled": false,
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||||
"tools": [],
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"topPEnabled": false,
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"top_p": 0.3,
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"user_prompt": "",
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"visual_files_var": ""
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}
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},
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"upstream": [
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"begin"
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]
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},
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"Agent:TangyWordsType": {
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"downstream": [
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"Message:FreshWallsStudy"
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],
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"obj": {
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||||
"component_name": "Agent",
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||||
"params": {
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"cite": true,
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||||
"delay_after_error": 1,
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||||
"description": "",
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||||
"exception_default_value": "",
|
||||
"exception_goto": [],
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||||
"exception_method": "",
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||||
"frequencyPenaltyEnabled": false,
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"frequency_penalty": 0.7,
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"llm_id": "qwen-turbo@Tongyi-Qianwen",
|
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"maxTokensEnabled": false,
|
||||
"max_retries": 3,
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"max_rounds": 1,
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"max_tokens": 256,
|
||||
"mcp": [],
|
||||
"message_history_window_size": 12,
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||||
"outputs": {
|
||||
"content": {
|
||||
"type": "string",
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||||
"value": ""
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||||
},
|
||||
"structured": {}
|
||||
},
|
||||
"presencePenaltyEnabled": false,
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||||
"presence_penalty": 0.4,
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"prompts": [
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{
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"content": "Search Plan: {Agent:LargeFliesMelt@content}\n\n\n\nAwait Response feedback:{UserFillUp:GoldBroomsRelate@instructions}\n",
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"role": "user"
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}
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],
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"sys_prompt": "<role>\nYou are the Search Agent.\nYour job is to execute the approved Search Plan, integrate the Await Response feedback, retrieve evidence, and produce a well-grounded answer.\n</role>\n<objectives>\nTranslate the plan + feedback into concrete searches.\nCollect diverse, trustworthy, and recent evidence meeting the plan’s evidence bar.\nSynthesize a concise answer; include citations next to claims they support.\nIf evidence is insufficient or conflicting, clearly state limitations and propose next steps.\n</objectives>\n <tools>\nRetrieval: You must use Retrieval to do the search.\n </tools>\n",
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||||
"temperature": 0.1,
|
||||
"temperatureEnabled": false,
|
||||
"tools": [
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||||
{
|
||||
"component_name": "Retrieval",
|
||||
"name": "Retrieval",
|
||||
"params": {
|
||||
"cross_languages": [],
|
||||
"description": "",
|
||||
"empty_response": "",
|
||||
"kb_ids": [],
|
||||
"keywords_similarity_weight": 0.7,
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||||
"outputs": {
|
||||
"formalized_content": {
|
||||
"type": "string",
|
||||
"value": ""
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||||
},
|
||||
"json": {
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||||
"type": "Array<Object>",
|
||||
"value": []
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||||
}
|
||||
},
|
||||
"rerank_id": "",
|
||||
"similarity_threshold": 0.2,
|
||||
"toc_enhance": false,
|
||||
"top_k": 1024,
|
||||
"top_n": 8,
|
||||
"use_kg": false
|
||||
}
|
||||
}
|
||||
],
|
||||
"topPEnabled": false,
|
||||
"top_p": 0.3,
|
||||
"user_prompt": "",
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||||
"visual_files_var": ""
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||||
}
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||||
},
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||||
"upstream": [
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||||
"UserFillUp:GoldBroomsRelate"
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||||
]
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||||
},
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||||
"Message:FreshWallsStudy": {
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"downstream": [],
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"obj": {
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||||
"component_name": "Message",
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||||
"params": {
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||||
"content": [
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"{Agent:TangyWordsType@content}"
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||||
]
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||||
}
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||||
},
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||||
"upstream": [
|
||||
"Agent:TangyWordsType"
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||||
]
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||||
},
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||||
"UserFillUp:GoldBroomsRelate": {
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||||
"downstream": [
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||||
"Agent:TangyWordsType"
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||||
],
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||||
"obj": {
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||||
"component_name": "UserFillUp",
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||||
"params": {
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||||
"enable_tips": true,
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||||
"inputs": {
|
||||
"instructions": {
|
||||
"name": "instructions",
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||||
"optional": false,
|
||||
"options": [],
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||||
"type": "paragraph"
|
||||
}
|
||||
},
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||||
"outputs": {
|
||||
"instructions": {
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||||
"name": "instructions",
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||||
"optional": false,
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||||
"options": [],
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||||
"type": "paragraph"
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||||
}
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||||
},
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||||
"tips": "Here is my search plan:\n{Agent:LargeFliesMelt@content}\nAre you okay with it?"
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}
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||||
},
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||||
"upstream": [
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||||
"Agent:LargeFliesMelt"
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||||
]
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||||
},
|
||||
"begin": {
|
||||
"downstream": [
|
||||
"Agent:LargeFliesMelt"
|
||||
],
|
||||
"obj": {
|
||||
"component_name": "Begin",
|
||||
"params": {}
|
||||
},
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||||
"upstream": []
|
||||
}
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||||
},
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||||
"globals": {
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||||
"sys.conversation_turns": 0,
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"sys.files": [],
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||||
"sys.query": "",
|
||||
"sys.user_id": ""
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||||
},
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"graph": {
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||||
"edges": [
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{
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||||
"data": {
|
||||
"isHovered": false
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||||
},
|
||||
"id": "xy-edge__beginstart-Agent:LargeFliesMeltend",
|
||||
"source": "begin",
|
||||
"sourceHandle": "start",
|
||||
"target": "Agent:LargeFliesMelt",
|
||||
"targetHandle": "end"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"isHovered": false
|
||||
},
|
||||
"id": "xy-edge__Agent:LargeFliesMeltstart-UserFillUp:GoldBroomsRelateend",
|
||||
"source": "Agent:LargeFliesMelt",
|
||||
"sourceHandle": "start",
|
||||
"target": "UserFillUp:GoldBroomsRelate",
|
||||
"targetHandle": "end"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"isHovered": false
|
||||
},
|
||||
"id": "xy-edge__UserFillUp:GoldBroomsRelatestart-Agent:TangyWordsTypeend",
|
||||
"source": "UserFillUp:GoldBroomsRelate",
|
||||
"sourceHandle": "start",
|
||||
"target": "Agent:TangyWordsType",
|
||||
"targetHandle": "end"
|
||||
},
|
||||
{
|
||||
"id": "xy-edge__Agent:TangyWordsTypetool-Tool:NastyBatsGoend",
|
||||
"source": "Agent:TangyWordsType",
|
||||
"sourceHandle": "tool",
|
||||
"target": "Tool:NastyBatsGo",
|
||||
"targetHandle": "end"
|
||||
},
|
||||
{
|
||||
"id": "xy-edge__Agent:TangyWordsTypestart-Message:FreshWallsStudyend",
|
||||
"source": "Agent:TangyWordsType",
|
||||
"sourceHandle": "start",
|
||||
"target": "Message:FreshWallsStudy",
|
||||
"targetHandle": "end"
|
||||
}
|
||||
],
|
||||
"nodes": [
|
||||
{
|
||||
"data": {
|
||||
"label": "Begin",
|
||||
"name": "begin"
|
||||
},
|
||||
"dragging": false,
|
||||
"id": "begin",
|
||||
"measured": {
|
||||
"height": 50,
|
||||
"width": 200
|
||||
},
|
||||
"position": {
|
||||
"x": 154.9008789064451,
|
||||
"y": 119.51001744285344
|
||||
},
|
||||
"selected": false,
|
||||
"sourcePosition": "left",
|
||||
"targetPosition": "right",
|
||||
"type": "beginNode"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"form": {
|
||||
"cite": true,
|
||||
"delay_after_error": 1,
|
||||
"description": "",
|
||||
"exception_default_value": "",
|
||||
"exception_goto": [],
|
||||
"exception_method": "",
|
||||
"frequencyPenaltyEnabled": false,
|
||||
"frequency_penalty": 0.7,
|
||||
"llm_id": "qwen-turbo@Tongyi-Qianwen",
|
||||
"maxTokensEnabled": false,
|
||||
"max_retries": 3,
|
||||
"max_rounds": 1,
|
||||
"max_tokens": 256,
|
||||
"mcp": [],
|
||||
"message_history_window_size": 12,
|
||||
"outputs": {
|
||||
"content": {
|
||||
"type": "string",
|
||||
"value": ""
|
||||
},
|
||||
"structured": {}
|
||||
},
|
||||
"presencePenaltyEnabled": false,
|
||||
"presence_penalty": 0.4,
|
||||
"prompts": [
|
||||
{
|
||||
"content": "User query:{sys.query}",
|
||||
"role": "user"
|
||||
}
|
||||
],
|
||||
"sys_prompt": "<role>\nYou are the Planning Agent in a multi-agent RAG workflow.\nYour sole job is to design a crisp, executable Search Plan for the next agent. Do not search or answer the user’s question.\n</role>\n<objectives>\nUnderstand the user’s task and decompose it into evidence-seeking steps.\nProduce high-quality queries and retrieval settings tailored to the task type (fact lookup, multi-hop reasoning, comparison, statistics, how-to, etc.).\nIdentify missing information that would materially change the plan (≤3 concise questions).\nOptimize for source trustworthiness, diversity, and recency; define stopping criteria to avoid over-searching.\nAnswer in 150 words.\n<objectives>",
|
||||
"temperature": 0.1,
|
||||
"temperatureEnabled": false,
|
||||
"tools": [],
|
||||
"topPEnabled": false,
|
||||
"top_p": 0.3,
|
||||
"user_prompt": "",
|
||||
"visual_files_var": ""
|
||||
},
|
||||
"label": "Agent",
|
||||
"name": "Planning Agent"
|
||||
},
|
||||
"dragging": false,
|
||||
"id": "Agent:LargeFliesMelt",
|
||||
"measured": {
|
||||
"height": 90,
|
||||
"width": 200
|
||||
},
|
||||
"position": {
|
||||
"x": 443.96309330796714,
|
||||
"y": 104.61370811205677
|
||||
},
|
||||
"selected": false,
|
||||
"sourcePosition": "right",
|
||||
"targetPosition": "left",
|
||||
"type": "agentNode"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"form": {
|
||||
"enable_tips": true,
|
||||
"inputs": {
|
||||
"instructions": {
|
||||
"name": "instructions",
|
||||
"optional": false,
|
||||
"options": [],
|
||||
"type": "paragraph"
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"instructions": {
|
||||
"name": "instructions",
|
||||
"optional": false,
|
||||
"options": [],
|
||||
"type": "paragraph"
|
||||
}
|
||||
},
|
||||
"tips": "Here is my search plan:\n{Agent:LargeFliesMelt@content}\nAre you okay with it?"
|
||||
},
|
||||
"label": "UserFillUp",
|
||||
"name": "Await Response"
|
||||
},
|
||||
"dragging": false,
|
||||
"id": "UserFillUp:GoldBroomsRelate",
|
||||
"measured": {
|
||||
"height": 50,
|
||||
"width": 200
|
||||
},
|
||||
"position": {
|
||||
"x": 683.3409492927474,
|
||||
"y": 116.76274137645598
|
||||
},
|
||||
"selected": false,
|
||||
"sourcePosition": "right",
|
||||
"targetPosition": "left",
|
||||
"type": "ragNode"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"form": {
|
||||
"cite": true,
|
||||
"delay_after_error": 1,
|
||||
"description": "",
|
||||
"exception_default_value": "",
|
||||
"exception_goto": [],
|
||||
"exception_method": "",
|
||||
"frequencyPenaltyEnabled": false,
|
||||
"frequency_penalty": 0.7,
|
||||
"llm_id": "qwen-turbo@Tongyi-Qianwen",
|
||||
"maxTokensEnabled": false,
|
||||
"max_retries": 3,
|
||||
"max_rounds": 1,
|
||||
"max_tokens": 256,
|
||||
"mcp": [],
|
||||
"message_history_window_size": 12,
|
||||
"outputs": {
|
||||
"content": {
|
||||
"type": "string",
|
||||
"value": ""
|
||||
},
|
||||
"structured": {}
|
||||
},
|
||||
"presencePenaltyEnabled": false,
|
||||
"presence_penalty": 0.4,
|
||||
"prompts": [
|
||||
{
|
||||
"content": "Search Plan: {Agent:LargeFliesMelt@content}\n\n\n\nAwait Response feedback:{UserFillUp:GoldBroomsRelate@instructions}\n",
|
||||
"role": "user"
|
||||
}
|
||||
],
|
||||
"sys_prompt": "<role>\nYou are the Search Agent.\nYour job is to execute the approved Search Plan, integrate the Await Response feedback, retrieve evidence, and produce a well-grounded answer.\n</role>\n<objectives>\nTranslate the plan + feedback into concrete searches.\nCollect diverse, trustworthy, and recent evidence meeting the plan’s evidence bar.\nSynthesize a concise answer; include citations next to claims they support.\nIf evidence is insufficient or conflicting, clearly state limitations and propose next steps.\n</objectives>\n <tools>\nRetrieval: You must use Retrieval to do the search.\n </tools>\n",
|
||||
"temperature": 0.1,
|
||||
"temperatureEnabled": false,
|
||||
"tools": [
|
||||
{
|
||||
"component_name": "Retrieval",
|
||||
"name": "Retrieval",
|
||||
"params": {
|
||||
"cross_languages": [],
|
||||
"description": "",
|
||||
"empty_response": "",
|
||||
"kb_ids": [],
|
||||
"keywords_similarity_weight": 0.7,
|
||||
"outputs": {
|
||||
"formalized_content": {
|
||||
"type": "string",
|
||||
"value": ""
|
||||
},
|
||||
"json": {
|
||||
"type": "Array<Object>",
|
||||
"value": []
|
||||
}
|
||||
},
|
||||
"rerank_id": "",
|
||||
"similarity_threshold": 0.2,
|
||||
"toc_enhance": false,
|
||||
"top_k": 1024,
|
||||
"top_n": 8,
|
||||
"use_kg": false
|
||||
}
|
||||
}
|
||||
],
|
||||
"topPEnabled": false,
|
||||
"top_p": 0.3,
|
||||
"user_prompt": "",
|
||||
"visual_files_var": ""
|
||||
},
|
||||
"label": "Agent",
|
||||
"name": "Search Agent"
|
||||
},
|
||||
"dragging": false,
|
||||
"id": "Agent:TangyWordsType",
|
||||
"measured": {
|
||||
"height": 90,
|
||||
"width": 200
|
||||
},
|
||||
"position": {
|
||||
"x": 944.6411255659472,
|
||||
"y": 99.84499066368488
|
||||
},
|
||||
"selected": true,
|
||||
"sourcePosition": "right",
|
||||
"targetPosition": "left",
|
||||
"type": "agentNode"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"form": {
|
||||
"description": "This is an agent for a specific task.",
|
||||
"user_prompt": "This is the order you need to send to the agent."
|
||||
},
|
||||
"label": "Tool",
|
||||
"name": "flow.tool_0"
|
||||
},
|
||||
"id": "Tool:NastyBatsGo",
|
||||
"measured": {
|
||||
"height": 50,
|
||||
"width": 200
|
||||
},
|
||||
"position": {
|
||||
"x": 862.6411255659472,
|
||||
"y": 239.84499066368488
|
||||
},
|
||||
"sourcePosition": "right",
|
||||
"targetPosition": "left",
|
||||
"type": "toolNode"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"form": {
|
||||
"content": [
|
||||
"{Agent:TangyWordsType@content}"
|
||||
]
|
||||
},
|
||||
"label": "Message",
|
||||
"name": "Message"
|
||||
},
|
||||
"dragging": false,
|
||||
"id": "Message:FreshWallsStudy",
|
||||
"measured": {
|
||||
"height": 50,
|
||||
"width": 200
|
||||
},
|
||||
"position": {
|
||||
"x": 1216.7057997987163,
|
||||
"y": 120.48541298149814
|
||||
},
|
||||
"selected": false,
|
||||
"sourcePosition": "right",
|
||||
"targetPosition": "left",
|
||||
"type": "messageNode"
|
||||
}
|
||||
]
|
||||
},
|
||||
"history": [],
|
||||
"messages": [],
|
||||
"path": [],
|
||||
"retrieval": [],
|
||||
"variables": {}
|
||||
},
|
||||
"avatar":
|
||||
"data:image/png;base64,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"
|
||||
}
|
||||
Reference in New Issue
Block a user