Files
ragflow/agent/templates/user_interaction.json
LeonTung 1e8efa2631 chore(template): update agent template's title (#11201)
### What problem does this PR solve?

Update title

### Type of change

- [x] Other (please describe):
2025-11-12 12:53:28 +08:00

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{
"id": 27,
"title": {
"en": "Interactive Agent",
"zh": "可交互的 Agent"
},
"description": {
"en": "During the Agents execution, users can actively intervene and interact with the Agent to adjust or guide its output, ensuring the final result aligns with their intentions.",
"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。"
},
"canvas_type": "Agent",
"dsl": {
"components": {
"Agent:LargeFliesMelt": {
"downstream": [
"UserFillUp:GoldBroomsRelate"
],
"obj": {
"component_name": "Agent",
"params": {
"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 users question.\n</role>\n<objectives>\nUnderstand the users 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": ""
}
},
"upstream": [
"begin"
]
},
"Agent:TangyWordsType": {
"downstream": [
"Message:FreshWallsStudy"
],
"obj": {
"component_name": "Agent",
"params": {
"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 plans 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": ""
}
},
"upstream": [
"UserFillUp:GoldBroomsRelate"
]
},
"Message:FreshWallsStudy": {
"downstream": [],
"obj": {
"component_name": "Message",
"params": {
"content": [
"{Agent:TangyWordsType@content}"
]
}
},
"upstream": [
"Agent:TangyWordsType"
]
},
"UserFillUp:GoldBroomsRelate": {
"downstream": [
"Agent:TangyWordsType"
],
"obj": {
"component_name": "UserFillUp",
"params": {
"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?"
}
},
"upstream": [
"Agent:LargeFliesMelt"
]
},
"begin": {
"downstream": [
"Agent:LargeFliesMelt"
],
"obj": {
"component_name": "Begin",
"params": {}
},
"upstream": []
}
},
"globals": {
"sys.conversation_turns": 0,
"sys.files": [],
"sys.query": "",
"sys.user_id": ""
},
"graph": {
"edges": [
{
"data": {
"isHovered": false
},
"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 users question.\n</role>\n<objectives>\nUnderstand the users 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 plans 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":
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}