diff --git a/agent/templates/choose_your_knowledge_base_agent.json b/agent/templates/choose_your_knowledge_base_agent.json
deleted file mode 100644
index a4b7ac9379..0000000000
--- a/agent/templates/choose_your_knowledge_base_agent.json
+++ /dev/null
@@ -1,422 +0,0 @@
-{
- "id": 19,
- "title": {
- "en": "Choose Your Knowledge Base Agent",
- "de": "Wählen Sie Ihren Wissensdatenbank Agenten",
- "zh": "选择知识库智能体"},
- "description": {
- "en": "This Agent generates responses solely from the specified dataset (knowledge base). You are required to select a knowledge base from the dropdown when running the Agent.",
- "de": "Dieser Agent erzeugt Antworten ausschließlich aus dem angegebenen Datensatz (Wissensdatenbank). Beim Ausführen des Agents müssen Sie eine Wissensdatenbank aus dem Dropdown-Menü auswählen.",
- "zh": "本工作流仅根据指定知识库内容生成回答。运行时，请在下拉菜单选择需要查询的知识库。"},
- "canvas_type": "Agent",
- "dsl": {
- "components": {
- "Agent:BraveParksJoke": {
- "downstream": [
- "Message:HotMelonsObey"
- ],
- "obj": {
- "component_name": "Agent",
- "params": {
- "delay_after_error": 1,
- "description": "",
- "exception_default_value": "",
- "exception_goto": [],
- "exception_method": "",
- "frequencyPenaltyEnabled": false,
- "frequency_penalty": 0.7,
- "llm_id": "deepseek-chat@DeepSeek",
- "maxTokensEnabled": false,
- "max_retries": 3,
- "max_rounds": 1,
- "max_tokens": 256,
- "mcp": [],
- "message_history_window_size": 12,
- "outputs": {
- "content": {
- "type": "string",
- "value": ""
- }
- },
- "presencePenaltyEnabled": false,
- "presence_penalty": 0.4,
- "prompts": [
- {
- "content": "{sys.query}",
- "role": "user"
- }
- ],
- "sys_prompt": "#Role\nYou are a **Docs QA Agent**, a specialized knowledge base assistant responsible for providing accurate answers based strictly on the connected documentation repository.\n\n# Core Principles\n1. **Rapid Output**\nRetrieve and answer questions directly from the knowledge base using the retrieval tool. Immediately return results upon successful retrieval without additional reflection rounds. Prioritize rapid output even before reaching maximum iteration limits.\n2. **Knowledge Base Only**: Answer questions EXCLUSIVELY based on information retrieved from the connected knowledge base.\n3. **No Content Creation**: Never generate, infer, or create information that is not explicitly present in the retrieved documents.\n4. **Source Transparency**: Always indicate when information comes from the knowledge base vs. when it's unavailable.\n5. **Accuracy Over Completeness**: Prefer incomplete but accurate answers over complete but potentially inaccurate ones.\n# Response Guidelines\n## When Information is Available\n- Provide direct answers based on retrieved content\n- Quote relevant sections when helpful\n- Cite the source document/section if available\n- Use phrases like: \"According to the documentation...\" or \"Based on the knowledge base...\"\n## When Information is Unavailable\n- Clearly state: \"I cannot find this information in the current knowledge base.\"\n- Do NOT attempt to fill gaps with general knowledge\n- Suggest alternative questions that might be covered in the docs\n- Use phrases like: \"The documentation does not cover...\" or \"This information is not available in the knowledge base.\"\n# Response Format\n```markdown\n## Answer\n[Your response based strictly on knowledge base content]\n**Always do these:**\n- Use the Retrieval tool for every question\n- Be transparent about information availability\n- Stick to documented facts only\n- Acknowledge knowledge base limitations",
- "temperature": 0.1,
- "temperatureEnabled": true,
- "tools": [
- {
- "component_name": "Retrieval",
- "name": "Retrieval",
- "params": {
- "cross_languages": [],
- "description": "Retrieve from the knowledge bases.",
- "empty_response": "",
- "kb_ids": [
- "begin@knowledge base"
- ],
- "keywords_similarity_weight": 0.7,
- "outputs": {
- "formalized_content": {
- "type": "string",
- "value": ""
- }
- },
- "rerank_id": "",
- "similarity_threshold": 0.2,
- "top_k": 1024,
- "top_n": 8,
- "use_kg": false
- }
- }
- ],
- "topPEnabled": false,
- "top_p": 0.3,
- "user_prompt": "",
- "visual_files_var": ""
- }
- },
- "upstream": [
- "begin"
- ]
- },
- "Message:HotMelonsObey": {
- "downstream": [],
- "obj": {
- "component_name": "Message",
- "params": {
- "content": [
- "{Agent:BraveParksJoke@content}"
- ]
- }
- },
- "upstream": [
- "Agent:BraveParksJoke"
- ]
- },
- "begin": {
- "downstream": [
- "Agent:BraveParksJoke"
- ],
- "obj": {
- "component_name": "Begin",
- "params": {
- "enablePrologue": true,
- "inputs": {
- "knowledge base": {
- "name": "knowledge base",
- "optional": false,
- "options": [
- "knowledge base 1",
- "knowledge base 2",
- "knowledge base 3"
- ],
- "type": "options"
- }
- },
- "mode": "conversational",
- "prologue": "Hi! I'm your retrieval assistant. What do you want to ask?"
- }
- },
- "upstream": []
- }
- },
- "globals": {
- "sys.conversation_turns": 0,
- "sys.files": [],
- "sys.query": "",
- "sys.user_id": ""
- },
- "graph": {
- "edges": [
- {
- "data": {
- "isHovered": false
- },
- "id": "xy-edge__beginstart-Agent:BraveParksJokeend",
- "selected": false,
- "source": "begin",
- "sourceHandle": "start",
- "target": "Agent:BraveParksJoke",
- "targetHandle": "end"
- },
- {
- "data": {
- "isHovered": false
- },
- "id": "xy-edge__Agent:BraveParksJoketool-Tool:TangyWolvesDreamend",
- "source": "Agent:BraveParksJoke",
- "sourceHandle": "tool",
- "target": "Tool:TangyWolvesDream",
- "targetHandle": "end"
- },
- {
- "data": {
- "isHovered": false
- },
- "id": "xy-edge__Agent:BraveParksJokestart-Message:HotMelonsObeyend",
- "source": "Agent:BraveParksJoke",
- "sourceHandle": "start",
- "target": "Message:HotMelonsObey",
- "targetHandle": "end"
- }
- ],
- "nodes": [
- {
- "data": {
- "form": {
- "enablePrologue": true,
- "inputs": {
- "knowledge base": {
- "name": "knowledge base",
- "optional": false,
- "options": [
- "knowledge base 1",
- "knowledge base 2",
- "knowledge base 3"
- ],
- "type": "options"
- }
- },
- "mode": "conversational",
- "prologue": "Hi! I'm your retrieval assistant. What do you want to ask?"
- },
- "label": "Begin",
- "name": "begin"
- },
- "dragging": false,
- "id": "begin",
- "measured": {
- "height": 76,
- "width": 200
- },
- "position": {
- "x": 174.93384234796846,
- "y": -272.9638317458806
- },
- "selected": false,
- "sourcePosition": "left",
- "targetPosition": "right",
- "type": "beginNode"
- },
- {
- "data": {
- "form": {
- "delay_after_error": 1,
- "description": "",
- "exception_default_value": "",
- "exception_goto": [],
- "exception_method": "",
- "frequencyPenaltyEnabled": false,
- "frequency_penalty": 0.7,
- "llm_id": "deepseek-chat@DeepSeek",
- "maxTokensEnabled": false,
- "max_retries": 3,
- "max_rounds": 1,
- "max_tokens": 256,
- "mcp": [],
- "message_history_window_size": 12,
- "outputs": {
- "content": {
- "type": "string",
- "value": ""
- }
- },
- "presencePenaltyEnabled": false,
- "presence_penalty": 0.4,
- "prompts": [
- {
- "content": "{sys.query}",
- "role": "user"
- }
- ],
- "sys_prompt": "#Role\nYou are a **Docs QA Agent**, a specialized knowledge base assistant responsible for providing accurate answers based strictly on the connected documentation repository.\n\n# Core Principles\n1. **Rapid Output**\nRetrieve and answer questions directly from the knowledge base using the retrieval tool. Immediately return results upon successful retrieval without additional reflection rounds. Prioritize rapid output even before reaching maximum iteration limits.\n2. **Knowledge Base Only**: Answer questions EXCLUSIVELY based on information retrieved from the connected knowledge base.\n3. **No Content Creation**: Never generate, infer, or create information that is not explicitly present in the retrieved documents.\n4. **Source Transparency**: Always indicate when information comes from the knowledge base vs. when it's unavailable.\n5. **Accuracy Over Completeness**: Prefer incomplete but accurate answers over complete but potentially inaccurate ones.\n# Response Guidelines\n## When Information is Available\n- Provide direct answers based on retrieved content\n- Quote relevant sections when helpful\n- Cite the source document/section if available\n- Use phrases like: \"According to the documentation...\" or \"Based on the knowledge base...\"\n## When Information is Unavailable\n- Clearly state: \"I cannot find this information in the current knowledge base.\"\n- Do NOT attempt to fill gaps with general knowledge\n- Suggest alternative questions that might be covered in the docs\n- Use phrases like: \"The documentation does not cover...\" or \"This information is not available in the knowledge base.\"\n# Response Format\n```markdown\n## Answer\n[Your response based strictly on knowledge base content]\n**Always do these:**\n- Use the Retrieval tool for every question\n- Be transparent about information availability\n- Stick to documented facts only\n- Acknowledge knowledge base limitations",
- "temperature": 0.1,
- "temperatureEnabled": true,
- "tools": [
- {
- "component_name": "Retrieval",
- "name": "Retrieval",
- "params": {
- "cross_languages": [],
- "description": "Retrieve from the knowledge bases.",
- "empty_response": "",
- "kb_ids": [
- "begin@knowledge base"
- ],
- "keywords_similarity_weight": 0.7,
- "outputs": {
- "formalized_content": {
- "type": "string",
- "value": ""
- }
- },
- "rerank_id": "",
- "similarity_threshold": 0.2,
- "top_k": 1024,
- "top_n": 8,
- "use_kg": false
- }
- }
- ],
- "topPEnabled": false,
- "top_p": 0.3,
- "user_prompt": "",
- "visual_files_var": ""
- },
- "label": "Agent",
- "name": "Agent"
- },
- "dragging": false,
- "id": "Agent:BraveParksJoke",
- "measured": {
- "height": 84,
- "width": 200
- },
- "position": {
- "x": 699.8147585743118,
- "y": -512.1229013834202
- },
- "selected": false,
- "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:TangyWolvesDream",
- "measured": {
- "height": 48,
- "width": 200
- },
- "position": {
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- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "toolNode"
- },
- {
- "data": {
- "form": {
- "content": [
- "{Agent:BraveParksJoke@content}"
- ]
- },
- "label": "Message",
- "name": "Message"
- },
- "id": "Message:HotMelonsObey",
- "measured": {
- "height": 56,
- "width": 200
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- "position": {
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- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "messageNode"
- },
- {
- "data": {
- "form": {
- "text": "Configure the dropdown menu with your knowledge bases for retrieval."
- },
- "label": "Note",
- "name": "Note: Begin"
- },
- "dragHandle": ".note-drag-handle",
- "id": "Note:CurlyGoatsRun",
- "measured": {
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- "position": {
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- },
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "noteNode"
- },
- {
- "data": {
- "form": {
- "text": "The Agent will only retrieve from the selected knowledge base and use this content to generate responses.\n\nThe Agent prioritizes rapid response per system prompt configuration. Adjust reflection rounds by modifying the system prompt or via Agent > Advanced Settings > Max Rounds."
- },
- "label": "Note",
- "name": "Note: Agent"
- },
- "dragHandle": ".note-drag-handle",
- "dragging": false,
- "height": 186,
- "id": "Note:GentleShowersAct",
- "measured": {
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- "data": {
- "form": {
- "text": "This Agent generates responses solely from the specified dataset (knowledge base). \nYou are required to select a knowledge base from the dropdown when running the Agent."
- },
- "label": "Note",
- "name": "Workflow description"
- },
- "dragHandle": ".note-drag-handle",
- "dragging": false,
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- "id": "Note:FineCandlesDig",
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- "history": [],
- "messages": [],
- "path": [],
- "retrieval": []
- },
- "avatar": 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"
-}
\ No newline at end of file
diff --git a/agent/templates/choose_your_knowledge_base_workflow.json b/agent/templates/choose_your_knowledge_base_workflow.json
deleted file mode 100644
index 79886ed358..0000000000
--- a/agent/templates/choose_your_knowledge_base_workflow.json
+++ /dev/null
@@ -1,440 +0,0 @@
-{
- "id": 18,
- "title": {
- "en": "Choose Your Knowledge Base Workflow",
- "de": "Wählen Sie Ihren Wissensdatenbank Workflow",
- "zh": "选择知识库工作流"},
- "description": {
- "en": "This Agent generates responses solely from the specified dataset (knowledge base). You are required to select a knowledge base from the dropdown when running the Agent.",
- "de": "Dieser Agent erzeugt Antworten ausschließlich aus dem angegebenen Datensatz (Wissensdatenbank). Beim Ausführen des Agents müssen Sie eine Wissensdatenbank aus dem Dropdown-Menü auswählen.",
- "zh": "本工作流仅根据指定知识库内容生成回答。运行时，请在下拉菜单选择需要查询的知识库。"},
- "canvas_type": "Other",
- "dsl": {
- "components": {
- "Agent:ProudDingosShout": {
- "downstream": [
- "Message:DarkRavensType"
- ],
- "obj": {
- "component_name": "Agent",
- "params": {
- "delay_after_error": 1,
- "description": "",
- "exception_default_value": "",
- "exception_goto": [],
- "exception_method": "",
- "frequencyPenaltyEnabled": false,
- "frequency_penalty": 0.7,
- "llm_id": "deepseek-chat@DeepSeek",
- "maxTokensEnabled": false,
- "max_retries": 3,
- "max_rounds": 1,
- "max_tokens": 256,
- "mcp": [],
- "message_history_window_size": 12,
- "outputs": {
- "content": {
- "type": "string",
- "value": ""
- }
- },
- "presencePenaltyEnabled": false,
- "presence_penalty": 0.4,
- "prompts": [
- {
- "content": "User's query: {sys.query}\n\nRetrieval content: {Retrieval:RudeCyclesKneel@formalized_content}",
- "role": "user"
- }
- ],
- "sys_prompt": "# Role\nYou are the **Docs QA Agent**, a specialized knowledge base assistant responsible for providing accurate answers based strictly on the connected documentation repository.\n# Core Principles\n1. **Knowledge Base Only**: Answer questions EXCLUSIVELY based on information retrieved from the connected knowledge base.\n2. **No Content Creation**: Never generate, infer, or create information that is not explicitly present in the retrieved documents.\n3. **Source Transparency**: Always indicate when information comes from the knowledge base vs. when it's unavailable.\n4. **Accuracy Over Completeness**: Prefer incomplete but accurate answers over complete but potentially inaccurate ones.\n# Response Guidelines\n## When Information is Available\n- Provide direct answers based on retrieved content\n- Quote relevant sections when helpful\n- Cite the source document/section if available\n- Use phrases like: \"According to the documentation...\" or \"Based on the knowledge base...\"\n## When Information is Unavailable\n- Clearly state: \"I cannot find this information in the current knowledge base.\"\n- Do NOT attempt to fill gaps with general knowledge\n- Suggest alternative questions that might be covered in the docs\n- Use phrases like: \"The documentation does not cover...\" or \"This information is not available in the knowledge base.\"\n# Response Format\n```markdown\n## Answer\n[Your response based strictly on knowledge base content]\n**Always do these:**\n- Use the Retrieval tool for every question\n- Be transparent about information availability\n- Stick to documented facts only\n- Acknowledge knowledge base limitations",
- "temperature": 0.1,
- "temperatureEnabled": true,
- "tools": [],
- "topPEnabled": false,
- "top_p": 0.3,
- "user_prompt": "",
- "visual_files_var": ""
- }
- },
- "upstream": [
- "Retrieval:RudeCyclesKneel"
- ]
- },
- "Message:DarkRavensType": {
- "downstream": [],
- "obj": {
- "component_name": "Message",
- "params": {
- "content": [
- "{Agent:ProudDingosShout@content}"
- ]
- }
- },
- "upstream": [
- "Agent:ProudDingosShout"
- ]
- },
- "Retrieval:RudeCyclesKneel": {
- "downstream": [
- "Agent:ProudDingosShout"
- ],
- "obj": {
- "component_name": "Retrieval",
- "params": {
- "cross_languages": [],
- "empty_response": "",
- "kb_ids": [
- "begin@knowledge base"
- ],
- "keywords_similarity_weight": 0.7,
- "outputs": {
- "formalized_content": {
- "type": "string",
- "value": ""
- }
- },
- "query": "sys.query",
- "rerank_id": "",
- "similarity_threshold": 0.2,
- "top_k": 1024,
- "top_n": 8,
- "use_kg": false
- }
- },
- "upstream": [
- "begin"
- ]
- },
- "begin": {
- "downstream": [
- "Retrieval:RudeCyclesKneel"
- ],
- "obj": {
- "component_name": "Begin",
- "params": {
- "enablePrologue": true,
- "inputs": {
- "knowledge base": {
- "name": "knowledge base",
- "optional": false,
- "options": [
- "knowledge base 1",
- "knowledge base 2",
- "knowledge base 3"
- ],
- "type": "options"
- }
- },
- "mode": "conversational",
- "prologue": "Hi! I'm your retrieval assistant. What do you want to ask?"
- }
- },
- "upstream": []
- }
- },
- "globals": {
- "sys.conversation_turns": 0,
- "sys.files": [],
- "sys.query": "",
- "sys.user_id": ""
- },
- "graph": {
- "edges": [
- {
- "data": {
- "isHovered": false
- },
- "id": "xy-edge__beginstart-Retrieval:RudeCyclesKneelend",
- "source": "begin",
- "sourceHandle": "start",
- "target": "Retrieval:RudeCyclesKneel",
- "targetHandle": "end"
- },
- {
- "data": {
- "isHovered": false
- },
- "id": "xy-edge__Retrieval:RudeCyclesKneelstart-Agent:ProudDingosShoutend",
- "source": "Retrieval:RudeCyclesKneel",
- "sourceHandle": "start",
- "target": "Agent:ProudDingosShout",
- "targetHandle": "end"
- },
- {
- "data": {
- "isHovered": false
- },
- "id": "xy-edge__Agent:ProudDingosShoutstart-Message:DarkRavensTypeend",
- "source": "Agent:ProudDingosShout",
- "sourceHandle": "start",
- "target": "Message:DarkRavensType",
- "targetHandle": "end"
- }
- ],
- "nodes": [
- {
- "data": {
- "form": {
- "enablePrologue": true,
- "inputs": {
- "knowledge base": {
- "name": "knowledge base",
- "optional": false,
- "options": [
- "knowledge base 1",
- "knowledge base 2",
- "knowledge base 3"
- ],
- "type": "options"
- }
- },
- "mode": "conversational",
- "prologue": "Hi! I'm your retrieval assistant. What do you want to ask?"
- },
- "label": "Begin",
- "name": "begin"
- },
- "id": "begin",
- "measured": {
- "height": 76,
- "width": 200
- },
- "position": {
- "x": 50,
- "y": 200
- },
- "selected": false,
- "sourcePosition": "left",
- "targetPosition": "right",
- "type": "beginNode"
- },
- {
- "data": {
- "form": {
- "cross_languages": [],
- "empty_response": "",
- "kb_ids": [
- "begin@knowledge base"
- ],
- "keywords_similarity_weight": 0.7,
- "outputs": {
- "formalized_content": {
- "type": "string",
- "value": ""
- }
- },
- "query": "sys.query",
- "rerank_id": "",
- "similarity_threshold": 0.2,
- "top_k": 1024,
- "top_n": 8,
- "use_kg": false
- },
- "label": "Retrieval",
- "name": "Retrieval"
- },
- "dragging": false,
- "id": "Retrieval:RudeCyclesKneel",
- "measured": {
- "height": 96,
- "width": 200
- },
- "position": {
- "x": 368.9985951155415,
- "y": 188.91748618260078
- },
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "retrievalNode"
- },
- {
- "data": {
- "form": {
- "delay_after_error": 1,
- "description": "",
- "exception_default_value": "",
- "exception_goto": [],
- "exception_method": "",
- "frequencyPenaltyEnabled": false,
- "frequency_penalty": 0.7,
- "llm_id": "deepseek-chat@DeepSeek",
- "maxTokensEnabled": false,
- "max_retries": 3,
- "max_rounds": 1,
- "max_tokens": 256,
- "mcp": [],
- "message_history_window_size": 12,
- "outputs": {
- "content": {
- "type": "string",
- "value": ""
- }
- },
- "presencePenaltyEnabled": false,
- "presence_penalty": 0.4,
- "prompts": [
- {
- "content": "User's query: {sys.query}\n\nRetrieval content: {Retrieval:RudeCyclesKneel@formalized_content}",
- "role": "user"
- }
- ],
- "sys_prompt": "# Role\nYou are the **Docs QA Agent**, a specialized knowledge base assistant responsible for providing accurate answers based strictly on the connected documentation repository.\n# Core Principles\n1. **Knowledge Base Only**: Answer questions EXCLUSIVELY based on information retrieved from the connected knowledge base.\n2. **No Content Creation**: Never generate, infer, or create information that is not explicitly present in the retrieved documents.\n3. **Source Transparency**: Always indicate when information comes from the knowledge base vs. when it's unavailable.\n4. **Accuracy Over Completeness**: Prefer incomplete but accurate answers over complete but potentially inaccurate ones.\n# Response Guidelines\n## When Information is Available\n- Provide direct answers based on retrieved content\n- Quote relevant sections when helpful\n- Cite the source document/section if available\n- Use phrases like: \"According to the documentation...\" or \"Based on the knowledge base...\"\n## When Information is Unavailable\n- Clearly state: \"I cannot find this information in the current knowledge base.\"\n- Do NOT attempt to fill gaps with general knowledge\n- Suggest alternative questions that might be covered in the docs\n- Use phrases like: \"The documentation does not cover...\" or \"This information is not available in the knowledge base.\"\n# Response Format\n```markdown\n## Answer\n[Your response based strictly on knowledge base content]\n**Always do these:**\n- Use the Retrieval tool for every question\n- Be transparent about information availability\n- Stick to documented facts only\n- Acknowledge knowledge base limitations",
- "temperature": 0.1,
- "temperatureEnabled": true,
- "tools": [],
- "topPEnabled": false,
- "top_p": 0.3,
- "user_prompt": "",
- "visual_files_var": ""
- },
- "label": "Agent",
- "name": "Agent"
- },
- "dragging": false,
- "id": "Agent:ProudDingosShout",
- "measured": {
- "height": 86,
- "width": 200
- },
- "position": {
- "x": 732.9115613823421,
- "y": 173.29966667348305
- },
- "selected": true,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "agentNode"
- },
- {
- "data": {
- "form": {
- "content": [
- "{Agent:ProudDingosShout@content}"
- ]
- },
- "label": "Message",
- "name": "Message"
- },
- "dragging": false,
- "id": "Message:DarkRavensType",
- "measured": {
- "height": 56,
- "width": 200
- },
- "position": {
- "x": 1072.2594210214197,
- "y": 178.92078947906558
- },
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "messageNode"
- },
- {
- "data": {
- "form": {
- "text": "This Agent generates responses solely from the specified dataset (knowledge base). \nYou are required to select a knowledge base from the dropdown when running the Agent."
- },
- "label": "Note",
- "name": "Workflow description"
- },
- "dragHandle": ".note-drag-handle",
- "dragging": false,
- "height": 179,
- "id": "Note:HonestHatsSip",
- "measured": {
- "height": 179,
- "width": 345
- },
- "position": {
- "x": 79.79276047764881,
- "y": -41.86088007502428
- },
- "resizing": false,
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "noteNode",
- "width": 345
- },
- {
- "data": {
- "form": {
- "text": "Configure the dropdown menu with your knowledge bases for retrieval."
- },
- "label": "Note",
- "name": "Note: Begin"
- },
- "dragHandle": ".note-drag-handle",
- "id": "Note:BumpyWaspsAttend",
- "measured": {
- "height": 136,
- "width": 250
- },
- "position": {
- "x": 15,
- "y": 300
- },
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "noteNode"
- },
- {
- "data": {
- "form": {
- "text": "The workflow will retrieve data from the knowledge base selected in the dropdown menu."
- },
- "label": "Note",
- "name": "Note: Retrieval"
- },
- "dragHandle": ".note-drag-handle",
- "dragging": false,
- "id": "Note:AllFlowersDig",
- "measured": {
- "height": 136,
- "width": 250
- },
- "position": {
- "x": 361.872717062755,
- "y": 308.6265804950158
- },
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "noteNode"
- },
- {
- "data": {
- "form": {
- "text": "The Agent will generate responses according to the information retrieved from the chosen knowledge base."
- },
- "label": "Note",
- "name": "Note: Agent"
- },
- "dragHandle": ".note-drag-handle",
- "dragging": false,
- "id": "Note:UpsetGlassesDeny",
- "measured": {
- "height": 136,
- "width": 250
- },
- "position": {
- "x": 695.7034747745811,
- "y": 321.3328650385139
- },
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "noteNode"
- }
- ]
- },
- "history": [],
- "messages": [],
- "path": [],
- "retrieval": []
- },
- "avatar": 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"
-}
\ No newline at end of file
diff --git a/agent/templates/customer_review_analysis.json b/agent/templates/customer_feedback_dispatcher.json
similarity index 99%
rename from agent/templates/customer_review_analysis.json
rename to agent/templates/customer_feedback_dispatcher.json
index b6ecc76846..822a9dbd6a 100644
--- a/agent/templates/customer_review_analysis.json
+++ b/agent/templates/customer_feedback_dispatcher.json
@@ -2,13 +2,13 @@
{
"id": 11,
"title": {
- "en": "Customer Review Analysis",
- "de": "Kundenbewertungsanalyse",
- "zh": "客户评价分析"},
+ "en": "Customer feedback disptacher",
+ "de": "Feedback-Lotse",
+ "zh": "客户反馈协调员"},
"description": {
"en": "Automatically classify customer reviews using LLM (Large Language Model) and route them via email to the relevant departments.",
"de": "Klassifiziert automatisch Kundenbewertungen mithilfe von LLM (Large Language Model) und leitet sie per E-Mail an die zuständigen Abteilungen weiter.",
- "zh": "大模型将自动分类客户评价，并通过电子邮件将结果发送到相关部门。"},
+ "zh": "该模板将自动分类客户评价，并通过电子邮件将结果发送到相关部门。"},
"canvas_type": "Customer Support",
"dsl": {
"components": {
diff --git a/agent/templates/customer_service.json b/agent/templates/customer_service.json
deleted file mode 100644
index fc3704e535..0000000000
--- a/agent/templates/customer_service.json
+++ /dev/null
@@ -1,962 +0,0 @@
-
-{
- "id": 2,
- "title": {
- "en": "Multi-Agent Customer Support",
- "de": "Multi Agenten Kundensupport",
- "zh": "多智能体客服"},
- "description": {
- "en": "This is a multi-agent system for intelligent customer service processing based on user intent classification. It uses the lead-agent to identify the type of user needs, assign tasks to sub-agents for processing.",
- "de": "Dies ist ein Multi-Agenten-System für die intelligente Kundenservice-Verarbeitung basierend auf Benutzerabsichtsklassifizierung. Es verwendet den Haupt-Agenten zur Identifizierung der Art der Benutzerbedürfnisse und weist Aufgaben an Unter-Agenten zur Verarbeitung zu.",
- "zh": "多智能体系统，用于智能客服场景。基于用户意图分类，使用主智能体识别用户需求类型，并将任务分配给子智能体进行处理。"},
- "canvas_type": "Agent",
- "dsl": {
- "components": {
- "Agent:DullTownsHope": {
- "downstream": [
- "VariableAggregator:FuzzyBerriesFlow"
- ],
- "obj": {
- "component_name": "Agent",
- "params": {
- "delay_after_error": 1,
- "description": "",
- "exception_comment": "",
- "exception_default_value": "",
- "exception_goto": [],
- "exception_method": null,
- "frequencyPenaltyEnabled": false,
- "frequency_penalty": 0.3,
- "llm_id": "deepseek-chat@DeepSeek",
- "maxTokensEnabled": false,
- "max_retries": 3,
- "max_rounds": 5,
- "max_tokens": 4096,
- "mcp": [],
- "message_history_window_size": 12,
- "outputs": {
- "content": {
- "type": "string",
- "value": ""
- }
- },
- "parameter": "Balance",
- "presencePenaltyEnabled": false,
- "presence_penalty": 0.2,
- "prompts": [
- {
- "content": "The user query is {sys.query}",
- "role": "user"
- }
- ],
- "sys_prompt": "You are an empathetic mood-soothing assistant. \n\nYour role is to comfort and encourage users when they feel upset or frustrated. \n\n- Use a warm, kind, and understanding tone. \n\n- Focus on showing empathy and emotional support rather than solving the problem directly. \n\n- Always encourage users with positive and reassuring statements. ",
- "temperature": 0.5,
- "temperatureEnabled": true,
- "tools": [],
- "topPEnabled": false,
- "top_p": 0.85,
- "user_prompt": "",
- "visual_files_var": ""
- }
- },
- "upstream": [
- "Categorize:DullFriendsThank"
- ]
- },
- "Agent:KhakiSunsJudge": {
- "downstream": [
- "VariableAggregator:FuzzyBerriesFlow"
- ],
- "obj": {
- "component_name": "Agent",
- "params": {
- "delay_after_error": 1,
- "description": "",
- "exception_comment": "",
- "exception_default_value": "",
- "exception_goto": [],
- "exception_method": null,
- "frequencyPenaltyEnabled": false,
- "frequency_penalty": 0.7,
- "llm_id": "deepseek-chat@DeepSeek",
- "maxTokensEnabled": false,
- "max_retries": 3,
- "max_rounds": 5,
- "max_tokens": 256,
- "mcp": [],
- "message_history_window_size": 12,
- "outputs": {
- "content": {
- "type": "string",
- "value": ""
- }
- },
- "presencePenaltyEnabled": false,
- "presence_penalty": 0.4,
- "prompts": [
- {
- "content": "The user query is {sys.query}\n\nThe relevant document are {Retrieval:ShyPumasJoke@formalized_content}",
- "role": "user"
- }
- ],
- "sys_prompt": "You are a highly professional product information advisor. \n\nYour only mission is to provide accurate, factual, and structured answers to all product-related queries.\n\nAbsolutely no assumptions, guesses, or fabricated content are allowed. \n\n**Key Principles:**\n\n1. **Strict Database Reliance:** \n\n - Every answer must be based solely on the verified product information stored in the relevant documen.\n\n - You are NOT allowed to invent, speculate, or infer details beyond what is retrieved. \n\n - If you cannot find relevant data, respond with: *\"I cannot find this information in our official product database. Please check back later or provide more details for further search.\"*\n\n2. **Information Accuracy and Structure:** \n\n - Provide information in a clear, concise, and professional way. \n\n - Use bullet points or numbered lists if there are multiple key points (e.g., features, price, warranty, technical specifications). \n\n - Always specify the version or model number when applicable to avoid confusion.\n\n3. **Tone and Style:** \n\n - Maintain a polite, professional, and helpful tone at all times. \n\n - Avoid marketing exaggeration or promotional language; stay strictly factual. \n\n - Do not express personal opinions; only cite official product data.\n\n4. **User Guidance:** \n\n - If the user’s query is unclear or too broad, politely request clarification or guide them to provide more specific product details (e.g., product name, model, version). \n\n - Example: *\"Could you please specify the product model or category so I can retrieve the most relevant information for you?\"*\n\n5. **Response Length and Formatting:** \n\n - Keep each answer within 100–150 words for general queries. \n\n - For complex or multi-step explanations, you may extend to 200–250 words, but always remain clear and well-structured.\n\n6. **Critical Reminder:** \n\nYour authority and reliability depend entirely on the relevant document responses. Any fabricated, speculative, or unverified content will be considered a critical failure of your role.\n\n\n",
- "temperature": 0.1,
- "temperatureEnabled": true,
- "tools": [],
- "topPEnabled": false,
- "top_p": 0.3,
- "user_prompt": "",
- "visual_files_var": ""
- }
- },
- "upstream": [
- "Retrieval:ShyPumasJoke"
- ]
- },
- "Agent:TwelveOwlsWatch": {
- "downstream": [
- "VariableAggregator:FuzzyBerriesFlow"
- ],
- "obj": {
- "component_name": "Agent",
- "params": {
- "delay_after_error": 1,
- "description": "",
- "exception_comment": "",
- "exception_default_value": "",
- "exception_goto": [],
- "exception_method": null,
- "frequencyPenaltyEnabled": false,
- "frequency_penalty": 0.3,
- "llm_id": "deepseek-chat@DeepSeek",
- "maxTokensEnabled": false,
- "max_retries": 3,
- "max_rounds": 5,
- "max_tokens": 4096,
- "mcp": [],
- "message_history_window_size": 12,
- "outputs": {
- "content": {
- "type": "string",
- "value": ""
- }
- },
- "parameter": "Balance",
- "presencePenaltyEnabled": false,
- "presence_penalty": 0.2,
- "prompts": [
- {
- "content": "The user query is {sys.query}",
- "role": "user"
- }
- ],
- "sys_prompt": "You are a friendly and casual conversational assistant. \n\nYour primary goal is to engage users in light and enjoyable daily conversation. \n\n- Keep a natural, relaxed, and positive tone. \n\n- Avoid sensitive, controversial, or negative topics. \n\n- You may gently guide the conversation by introducing related casual topics if the user shows interest. \n\n",
- "temperature": 0.5,
- "temperatureEnabled": true,
- "tools": [],
- "topPEnabled": false,
- "top_p": 0.85,
- "user_prompt": "",
- "visual_files_var": ""
- }
- },
- "upstream": [
- "Categorize:DullFriendsThank"
- ]
- },
- "Categorize:DullFriendsThank": {
- "downstream": [
- "Message:BreezyDonutsHeal",
- "Agent:TwelveOwlsWatch",
- "Agent:DullTownsHope",
- "Retrieval:ShyPumasJoke"
- ],
- "obj": {
- "component_name": "Categorize",
- "params": {
- "category_description": {
- "1. contact": {
- "description": "This answer provide a specific contact information, like e-mail, phone number, wechat number, line number, twitter, discord, etc,.",
- "examples": [
- "My phone number is 203921\nkevinhu.hk@gmail.com\nThis is my discord number: johndowson_29384\n13212123432\n8379829"
- ],
- "to": [
- "Message:BreezyDonutsHeal"
- ]
- },
- "2. casual": {
- "description": "The question is not about the product usage, appearance and how it works. Just casual chat.",
- "examples": [
- "How are you doing?\nWhat is your name?\nAre you a robot?\nWhat's the weather?\nWill it rain?"
- ],
- "to": [
- "Agent:TwelveOwlsWatch"
- ]
- },
- "3. complain": {
- "description": "Complain even curse about the product or service you provide. But the comment is not specific enough.",
- "examples": [
- "How bad is it.\nIt's really sucks.\nDamn, for God's sake, can it be more steady?\nShit, I just can't use this shit.\nI can't stand it anymore."
- ],
- "to": [
- "Agent:DullTownsHope"
- ]
- },
- "4. product related": {
- "description": "The question is about the product usage, appearance and how it works.",
- "examples": [
- "Why it always beaming?\nHow to install it onto the wall?\nIt leaks, what to do?\nException: Can't connect to ES cluster\nHow to build the RAGFlow image from scratch"
- ],
- "to": [
- "Retrieval:ShyPumasJoke"
- ]
- }
- },
- "llm_id": "deepseek-chat@DeepSeek",
- "message_history_window_size": 1,
- "outputs": {
- "category_name": {
- "type": "string"
- }
- },
- "query": "sys.query",
- "temperature": "0.1"
- }
- },
- "upstream": [
- "begin"
- ]
- },
- "Message:BreezyDonutsHeal": {
- "downstream": [],
- "obj": {
- "component_name": "Message",
- "params": {
- "content": [
- "Okay, I've already write this down. What else I can do for you?",
- "Get it. What else I can do for you?",
- "Thanks for your trust! Our expert will contact ASAP. So, anything else I can do for you?",
- "Thanks! So, anything else I can do for you?"
- ]
- }
- },
- "upstream": [
- "Categorize:DullFriendsThank"
- ]
- },
- "Message:DryBusesCarry": {
- "downstream": [],
- "obj": {
- "component_name": "Message",
- "params": {
- "content": [
- "{VariableAggregator:FuzzyBerriesFlow@LLM_Response}"
- ]
- }
- },
- "upstream": [
- "VariableAggregator:FuzzyBerriesFlow"
- ]
- },
- "Retrieval:ShyPumasJoke": {
- "downstream": [
- "Agent:KhakiSunsJudge"
- ],
- "obj": {
- "component_name": "Retrieval",
- "params": {
- "cross_languages": [],
- "empty_response": "",
- "kb_ids": [],
- "keywords_similarity_weight": 0.7,
- "outputs": {
- "formalized_content": {
- "type": "string",
- "value": ""
- }
- },
- "query": "sys.query",
- "rerank_id": "",
- "similarity_threshold": 0.2,
- "top_k": 1024,
- "top_n": 8,
- "use_kg": false
- }
- },
- "upstream": [
- "Categorize:DullFriendsThank"
- ]
- },
- "VariableAggregator:FuzzyBerriesFlow": {
- "downstream": [
- "Message:DryBusesCarry"
- ],
- "obj": {
- "component_name": "VariableAggregator",
- "params": {
- "groups": [
- {
- "group_name": "LLM_Response",
- "type": "string",
- "variables": [
- {
- "value": "Agent:TwelveOwlsWatch@content"
- },
- {
- "value": "Agent:DullTownsHope@content"
- },
- {
- "value": "Agent:KhakiSunsJudge@content"
- }
- ]
- }
- ],
- "outputs": {
- "LLM_Response": {
- "type": "string"
- }
- }
- }
- },
- "upstream": [
- "Agent:DullTownsHope",
- "Agent:TwelveOwlsWatch",
- "Agent:KhakiSunsJudge"
- ]
- },
- "begin": {
- "downstream": [
- "Categorize:DullFriendsThank"
- ],
- "obj": {
- "component_name": "Begin",
- "params": {
- "enablePrologue": true,
- "inputs": {},
- "mode": "conversational",
- "prologue": "Hi! I'm an official AI customer service representative. How can I help you?"
- }
- },
- "upstream": []
- }
- },
- "globals": {
- "sys.conversation_turns": 0,
- "sys.files": [],
- "sys.query": "",
- "sys.user_id": ""
- },
- "graph": {
- "edges": [
- {
- "data": {
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- },
- "id": "xy-edge__beginstart-Categorize:DullFriendsThankend",
- "source": "begin",
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- "id": "xy-edge__Categorize:DullFriendsThanke4d754a5-a33e-4096-8648-8688e5474a15-Message:BreezyDonutsHealend",
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- "nodes": [
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- "inputs": {},
- "mode": "conversational",
- "prologue": "Hi! I'm an official AI customer service representative. How can I help you?"
- },
- "label": "Begin",
- "name": "begin"
- },
- "id": "begin",
- "measured": {
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- "width": 200
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- {
- "data": {
- "form": {
- "frequencyPenaltyEnabled": false,
- "frequency_penalty": 0.5,
- "items": [
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- "description": "This answer provide a specific contact information, like e-mail, phone number, wechat number, line number, twitter, discord, etc,.",
- "examples": [
- {
- "value": "My phone number is 203921\nkevinhu.hk@gmail.com\nThis is my discord number: johndowson_29384\n13212123432\n8379829"
- }
- ],
- "name": "1. contact",
- "uuid": "e4d754a5-a33e-4096-8648-8688e5474a15"
- },
- {
- "description": "The question is not about the product usage, appearance and how it works. Just casual chat.",
- "examples": [
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- "value": "How are you doing?\nWhat is your name?\nAre you a robot?\nWhat's the weather?\nWill it rain?"
- }
- ],
- "name": "2. casual",
- "uuid": "8cbf6ea3-a176-490d-9f8c-86373c932583"
- },
- {
- "description": "Complain even curse about the product or service you provide. But the comment is not specific enough.",
- "examples": [
- {
- "value": "How bad is it.\nIt's really sucks.\nDamn, for God's sake, can it be more steady?\nShit, I just can't use this shit.\nI can't stand it anymore."
- }
- ],
- "name": "3. complain",
- "uuid": "acc40a78-1b9e-4d2f-b5d6-64e01ab69269"
- },
- {
- "description": "The question is about the product usage, appearance and how it works.",
- "examples": [
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- "value": "Why it always beaming?\nHow to install it onto the wall?\nIt leaks, what to do?\nException: Can't connect to ES cluster\nHow to build the RAGFlow image from scratch"
- }
- ],
- "name": "4. product related",
- "uuid": "dfa5eead-9341-4f22-9236-068dbfb745e8"
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- ],
- "llm_id": "deepseek-chat@DeepSeek",
- "maxTokensEnabled": false,
- "max_tokens": 4096,
- "message_history_window_size": 1,
- "outputs": {
- "category_name": {
- "type": "string"
- }
- },
- "parameter": "Precise",
- "presencePenaltyEnabled": false,
- "presence_penalty": 0.5,
- "query": "sys.query",
- "temperature": "0.1",
- "temperatureEnabled": true,
- "topPEnabled": false,
- "top_p": 0.75
- },
- "label": "Categorize",
- "name": "Categorize"
- },
- "dragging": false,
- "id": "Categorize:DullFriendsThank",
- "measured": {
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- "width": 200
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- "content": [
- "Okay, I've already write this down. What else I can do for you?",
- "Get it. What else I can do for you?",
- "Thanks for your trust! Our expert will contact ASAP. So, anything else I can do for you?",
- "Thanks! So, anything else I can do for you?"
- ]
- },
- "label": "Message",
- "name": "What else?"
- },
- "dragging": false,
- "id": "Message:BreezyDonutsHeal",
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- "exception_default_value": "",
- "exception_goto": [],
- "exception_method": null,
- "frequencyPenaltyEnabled": false,
- "frequency_penalty": 0.3,
- "llm_id": "deepseek-chat@DeepSeek",
- "maxTokensEnabled": false,
- "max_retries": 3,
- "max_rounds": 5,
- "max_tokens": 4096,
- "mcp": [],
- "message_history_window_size": 12,
- "outputs": {
- "content": {
- "type": "string",
- "value": ""
- }
- },
- "parameter": "Balance",
- "presencePenaltyEnabled": false,
- "presence_penalty": 0.2,
- "prompts": [
- {
- "content": "The user query is {sys.query}",
- "role": "user"
- }
- ],
- "sys_prompt": "You are a friendly and casual conversational assistant. \n\nYour primary goal is to engage users in light and enjoyable daily conversation. \n\n- Keep a natural, relaxed, and positive tone. \n\n- Avoid sensitive, controversial, or negative topics. \n\n- You may gently guide the conversation by introducing related casual topics if the user shows interest. \n\n",
- "temperature": 0.5,
- "temperatureEnabled": true,
- "tools": [],
- "topPEnabled": false,
- "top_p": 0.85,
- "user_prompt": "",
- "visual_files_var": ""
- },
- "label": "Agent",
- "name": "Causal chat"
- },
- "dragging": false,
- "id": "Agent:TwelveOwlsWatch",
- "measured": {
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- "width": 200
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- "position": {
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- "y": 167.46311264481432
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- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "agentNode"
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- "description": "",
- "exception_comment": "",
- "exception_default_value": "",
- "exception_goto": [],
- "exception_method": null,
- "frequencyPenaltyEnabled": false,
- "frequency_penalty": 0.3,
- "llm_id": "deepseek-chat@DeepSeek",
- "maxTokensEnabled": false,
- "max_retries": 3,
- "max_rounds": 5,
- "max_tokens": 4096,
- "mcp": [],
- "message_history_window_size": 12,
- "outputs": {
- "content": {
- "type": "string",
- "value": ""
- }
- },
- "parameter": "Balance",
- "presencePenaltyEnabled": false,
- "presence_penalty": 0.2,
- "prompts": [
- {
- "content": "The user query is {sys.query}",
- "role": "user"
- }
- ],
- "sys_prompt": "You are an empathetic mood-soothing assistant. \n\nYour role is to comfort and encourage users when they feel upset or frustrated. \n\n- Use a warm, kind, and understanding tone. \n\n- Focus on showing empathy and emotional support rather than solving the problem directly. \n\n- Always encourage users with positive and reassuring statements. ",
- "temperature": 0.5,
- "temperatureEnabled": true,
- "tools": [],
- "topPEnabled": false,
- "top_p": 0.85,
- "user_prompt": "",
- "visual_files_var": ""
- },
- "label": "Agent",
- "name": "Soothe mood"
- },
- "dragging": false,
- "id": "Agent:DullTownsHope",
- "measured": {
- "height": 84,
- "width": 200
- },
- "position": {
- "x": 722.665715093248,
- "y": 281.3422183879642
- },
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "agentNode"
- },
- {
- "data": {
- "form": {
- "cross_languages": [],
- "empty_response": "",
- "kb_ids": [],
- "keywords_similarity_weight": 0.7,
- "outputs": {
- "formalized_content": {
- "type": "string",
- "value": ""
- }
- },
- "query": "sys.query",
- "rerank_id": "",
- "similarity_threshold": 0.2,
- "top_k": 1024,
- "top_n": 8,
- "use_kg": false
- },
- "label": "Retrieval",
- "name": "Search product info"
- },
- "dragging": false,
- "id": "Retrieval:ShyPumasJoke",
- "measured": {
- "height": 50,
- "width": 200
- },
- "position": {
- "x": 645.6873721057459,
- "y": 516.6923702571407
- },
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "retrievalNode"
- },
- {
- "data": {
- "form": {
- "delay_after_error": 1,
- "description": "",
- "exception_comment": "",
- "exception_default_value": "",
- "exception_goto": [],
- "exception_method": null,
- "frequencyPenaltyEnabled": false,
- "frequency_penalty": 0.7,
- "llm_id": "deepseek-chat@DeepSeek",
- "maxTokensEnabled": false,
- "max_retries": 3,
- "max_rounds": 5,
- "max_tokens": 256,
- "mcp": [],
- "message_history_window_size": 12,
- "outputs": {
- "content": {
- "type": "string",
- "value": ""
- }
- },
- "presencePenaltyEnabled": false,
- "presence_penalty": 0.4,
- "prompts": [
- {
- "content": "The user query is {sys.query}\n\nThe relevant document are {Retrieval:ShyPumasJoke@formalized_content}",
- "role": "user"
- }
- ],
- "sys_prompt": "You are a highly professional product information advisor. \n\nYour only mission is to provide accurate, factual, and structured answers to all product-related queries.\n\nAbsolutely no assumptions, guesses, or fabricated content are allowed. \n\n**Key Principles:**\n\n1. **Strict Database Reliance:** \n\n - Every answer must be based solely on the verified product information stored in the relevant documen.\n\n - You are NOT allowed to invent, speculate, or infer details beyond what is retrieved. \n\n - If you cannot find relevant data, respond with: *\"I cannot find this information in our official product database. Please check back later or provide more details for further search.\"*\n\n2. **Information Accuracy and Structure:** \n\n - Provide information in a clear, concise, and professional way. \n\n - Use bullet points or numbered lists if there are multiple key points (e.g., features, price, warranty, technical specifications). \n\n - Always specify the version or model number when applicable to avoid confusion.\n\n3. **Tone and Style:** \n\n - Maintain a polite, professional, and helpful tone at all times. \n\n - Avoid marketing exaggeration or promotional language; stay strictly factual. \n\n - Do not express personal opinions; only cite official product data.\n\n4. **User Guidance:** \n\n - If the user’s query is unclear or too broad, politely request clarification or guide them to provide more specific product details (e.g., product name, model, version). \n\n - Example: *\"Could you please specify the product model or category so I can retrieve the most relevant information for you?\"*\n\n5. **Response Length and Formatting:** \n\n - Keep each answer within 100–150 words for general queries. \n\n - For complex or multi-step explanations, you may extend to 200–250 words, but always remain clear and well-structured.\n\n6. **Critical Reminder:** \n\nYour authority and reliability depend entirely on the relevant document responses. Any fabricated, speculative, or unverified content will be considered a critical failure of your role.\n\n\n",
- "temperature": 0.1,
- "temperatureEnabled": true,
- "tools": [],
- "topPEnabled": false,
- "top_p": 0.3,
- "user_prompt": "",
- "visual_files_var": ""
- },
- "label": "Agent",
- "name": "Product info"
- },
- "dragging": false,
- "id": "Agent:KhakiSunsJudge",
- "measured": {
- "height": 84,
- "width": 200
- },
- "position": {
- "x": 726.580040161058,
- "y": 386.5448208363979
- },
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "agentNode"
- },
- {
- "data": {
- "form": {
- "text": "This is an intelligent customer service processing system workflow based on user intent classification. It uses LLM to identify user demand types and transfers them to the corresponding professional agent for processing."
- },
- "label": "Note",
- "name": "Workflow Overall Description"
- },
- "dragHandle": ".note-drag-handle",
- "dragging": false,
- "height": 171,
- "id": "Note:AllGuestsShow",
- "measured": {
- "height": 171,
- "width": 380
- },
- "position": {
- "x": -283.6407251474677,
- "y": 157.2943019466498
- },
- "resizing": false,
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "noteNode",
- "width": 380
- },
- {
- "data": {
- "form": {
- "text": "Here, product document snippets related to the user's question will be retrieved from the knowledge base first, and the relevant document snippets will be passed to the LLM together with the user's question."
- },
- "label": "Note",
- "name": "Product info Agent"
- },
- "dragHandle": ".note-drag-handle",
- "dragging": false,
- "height": 154,
- "id": "Note:IcyBooksCough",
- "measured": {
- "height": 154,
- "width": 370
- },
- "position": {
- "x": 1014.0959071234828,
- "y": 492.830874176321
- },
- "resizing": false,
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "noteNode",
- "width": 370
- },
- {
- "data": {
- "form": {
- "text": "Here, a text will be randomly selected for answering"
- },
- "label": "Note",
- "name": "What else？"
- },
- "dragHandle": ".note-drag-handle",
- "dragging": false,
- "id": "Note:AllThingsHide",
- "measured": {
- "height": 136,
- "width": 249
- },
- "position": {
- "x": 770.7060131788647,
- "y": -123.23496705283817
- },
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "noteNode"
- },
- {
- "data": {
- "form": {
- "groups": [
- {
- "group_name": "LLM_Response",
- "type": "string",
- "variables": [
- {
- "value": "Agent:TwelveOwlsWatch@content"
- },
- {
- "value": "Agent:DullTownsHope@content"
- },
- {
- "value": "Agent:KhakiSunsJudge@content"
- }
- ]
- }
- ],
- "outputs": {
- "LLM_Response": {
- "type": "string"
- }
- }
- },
- "label": "VariableAggregator",
- "name": "Variable aggregator"
- },
- "dragging": false,
- "id": "VariableAggregator:FuzzyBerriesFlow",
- "measured": {
- "height": 150,
- "width": 200
- },
- "position": {
- "x": 1061.596672609154,
- "y": 247.90496561846572
- },
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "variableAggregatorNode"
- },
- {
- "data": {
- "form": {
- "content": [
- "{VariableAggregator:FuzzyBerriesFlow@LLM_Response}"
- ]
- },
- "label": "Message",
- "name": "Response"
- },
- "dragging": false,
- "id": "Message:DryBusesCarry",
- "measured": {
- "height": 50,
- "width": 200
- },
- "position": {
- "x": 1364.5500382017049,
- "y": 296.59667260915404
- },
- "selected": true,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "messageNode"
- }
- ]
- },
- "history": [],
- "messages": [],
- "path": [],
- "retrieval": [],
- "variables": {}
- },
- "avatar": 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"
-}
\ No newline at end of file
diff --git a/agent/templates/customer_support.json b/agent/templates/customer_support.json
deleted file mode 100644
index 5eaa3789d6..0000000000
--- a/agent/templates/customer_support.json
+++ /dev/null
@@ -1,886 +0,0 @@
-
-{
- "id": 10,
- "title": {
- "en":"Customer Support",
- "de": "Kundensupport",
- "zh": "客户支持"},
- "description": {
- "en": "This is an intelligent customer service processing system workflow based on user intent classification. It uses LLM to identify user demand types and transfers them to the corresponding professional agent for processing.",
- "de": "Dies ist ein intelligentes Kundenservice-Verarbeitungssystem-Workflow basierend auf Benutzerabsichtsklassifizierung. Es verwendet LLM zur Identifizierung von Benutzeranforderungstypen und überträgt diese zur Verarbeitung an den entsprechenden professionellen Agenten.",
- "zh": "工作流系统，用于智能客服场景。基于用户意图分类。使用大模型识别用户需求类型，并将需求转移给相应的智能体进行处理。"},
- "canvas_type": "Customer Support",
- "dsl": {
- "components": {
- "Agent:DullTownsHope": {
- "downstream": [
- "Message:GreatDucksArgue"
- ],
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- "prompts": [
- {
- "content": "The user query is {sys.query}\n\nThe relevant document are {Retrieval:ShyPumasJoke@formalized_content}",
- "role": "user"
- }
- ],
- "sys_prompt": "You are a highly professional product information advisor. \n\nYour only mission is to provide accurate, factual, and structured answers to all product-related queries.\n\nAbsolutely no assumptions, guesses, or fabricated content are allowed. \n\n**Key Principles:**\n\n1. **Strict Database Reliance:** \n\n - Every answer must be based solely on the verified product information stored in the relevant documen.\n\n - You are NOT allowed to invent, speculate, or infer details beyond what is retrieved. \n\n - If you cannot find relevant data, respond with: *\"I cannot find this information in our official product database. Please check back later or provide more details for further search.\"*\n\n2. **Information Accuracy and Structure:** \n\n - Provide information in a clear, concise, and professional way. \n\n - Use bullet points or numbered lists if there are multiple key points (e.g., features, price, warranty, technical specifications). \n\n - Always specify the version or model number when applicable to avoid confusion.\n\n3. **Tone and Style:** \n\n - Maintain a polite, professional, and helpful tone at all times. \n\n - Avoid marketing exaggeration or promotional language; stay strictly factual. \n\n - Do not express personal opinions; only cite official product data.\n\n4. **User Guidance:** \n\n - If the user\u2019s query is unclear or too broad, politely request clarification or guide them to provide more specific product details (e.g., product name, model, version). \n\n - Example: *\"Could you please specify the product model or category so I can retrieve the most relevant information for you?\"*\n\n5. **Response Length and Formatting:** \n\n - Keep each answer within 100\u2013150 words for general queries. \n\n - For complex or multi-step explanations, you may extend to 200\u2013250 words, but always remain clear and well-structured.\n\n6. **Critical Reminder:** \n\nYour authority and reliability depend entirely on the relevant document responses. Any fabricated, speculative, or unverified content will be considered a critical failure of your role.\n\n\n",
- "temperature": 0.1,
- "temperatureEnabled": true,
- "tools": [],
- "topPEnabled": false,
- "top_p": 0.3,
- "user_prompt": "",
- "visual_files_var": ""
- },
- "label": "Agent",
- "name": "Product info"
- },
- "dragging": false,
- "id": "Agent:KhakiSunsJudge",
- "measured": {
- "height": 84,
- "width": 200
- },
- "position": {
- "x": 726.580040161058,
- "y": 386.5448208363979
- },
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "agentNode"
- },
- {
- "data": {
- "form": {
- "content": [
- "{Agent:TwelveOwlsWatch@content}{Agent:DullTownsHope@content}{Agent:KhakiSunsJudge@content}"
- ]
- },
- "label": "Message",
- "name": "Response"
- },
- "dragging": false,
- "id": "Message:GreatDucksArgue",
- "measured": {
- "height": 56,
- "width": 200
- },
- "position": {
- "x": 1073.6401719497055,
- "y": 279.1730925642852
- },
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "messageNode"
- },
- {
- "data": {
- "form": {
- "text": "This is an intelligent customer service processing system workflow based on user intent classification. It uses LLM to identify user demand types and transfers them to the corresponding professional agent for processing."
- },
- "label": "Note",
- "name": "Workflow Overall Description"
- },
- "dragHandle": ".note-drag-handle",
- "dragging": false,
- "height": 171,
- "id": "Note:AllGuestsShow",
- "measured": {
- "height": 171,
- "width": 380
- },
- "position": {
- "x": -283.6407251474677,
- "y": 157.2943019466498
- },
- "resizing": false,
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "noteNode",
- "width": 380
- },
- {
- "data": {
- "form": {
- "text": "Here, product document snippets related to the user's question will be retrieved from the knowledge base first, and the relevant document snippets will be passed to the LLM together with the user's question."
- },
- "label": "Note",
- "name": "Product info Agent"
- },
- "dragHandle": ".note-drag-handle",
- "dragging": false,
- "height": 154,
- "id": "Note:IcyBooksCough",
- "measured": {
- "height": 154,
- "width": 370
- },
- "position": {
- "x": 1014.0959071234828,
- "y": 492.830874176321
- },
- "resizing": false,
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "noteNode",
- "width": 370
- },
- {
- "data": {
- "form": {
- "text": "Here, a text will be randomly selected for answering"
- },
- "label": "Note",
- "name": "What else\uff1f"
- },
- "dragHandle": ".note-drag-handle",
- "dragging": false,
- "id": "Note:AllThingsHide",
- "measured": {
- "height": 136,
- "width": 249
- },
- "position": {
- "x": 770.7060131788647,
- "y": -123.23496705283817
- },
- "selected": false,
- "sourcePosition": "right",
- "targetPosition": "left",
- "type": "noteNode"
- }
- ]
- },
- "history": [],
- "messages": [],
- "path": [],
- "retrieval": []
- },
- "avatar": "data:image/png;base64,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"
-}
\ No newline at end of file
diff --git a/agent/templates/deep_research.json b/agent/templates/deep_research.json
index c1eff2a201..31a15c34b6 100644
--- a/agent/templates/deep_research.json
+++ b/agent/templates/deep_research.json
@@ -2,13 +2,13 @@
{
"id": 1,
"title": {
- "en": "Deep Research",
+ "en": "Deep research",
"de": "Tiefgehende Recherche",
- "zh": "深度研究"},
+ "zh": "Deep research"},
"description": {
- "en": "For professionals in sales, marketing, policy, or consulting, the Multi-Agent Deep Research Agent conducts structured, multi-step investigations across diverse sources and delivers consulting-style reports with clear citations.",
+ "en": "For professionals in sales, marketing, policy, or consulting, the Multi-Agent Deep research Agentic workflow conducts structured, multi-step investigations across diverse sources and delivers consulting-style reports with clear citations.",
"de": "Für Fachleute in Vertrieb, Marketing, Politik oder Beratung führt der Multi-Agenten-Tiefenforschungsagent strukturierte, mehrstufige Untersuchungen über verschiedene Quellen durch und liefert Berichte im Beratungsstil mit klaren Quellenangaben.",
- "zh": "专为销售、市场、政策或咨询领域的专业人士设计，多智能体的深度研究会结合多源信息进行结构化、多步骤地回答问题，并附带有清晰的引用。"},
+ "zh": "专为销售、市场、政策或咨询领域的专业人士设计，多智能体的 Deep research 会结合多源信息进行结构化、多步骤地回答问题，并附带有清晰的引用。"},
"canvas_type": "Recommended",
"dsl": {
"components": {
@@ -431,7 +431,7 @@
"visual_files_var": ""
},
"label": "Agent",
- "name": "Deep Research Agent"
+ "name": "Deep research Agent"
},
"dragging": false,
"id": "Agent:NewPumasLick",
@@ -692,10 +692,10 @@
{
"data": {
"form": {
- "text": "A Deep Research Agent built on a multi-agent architecture.\nMuch of the credit goes to Anthropic\u2019s blog post, which deeply inspired this design.\n\nhttps://www.anthropic.com/engineering/built-multi-agent-research-system"
+ "text": "A Deep research Agent built on a multi-agent architecture.\nMuch of the credit goes to Anthropic\u2019s blog post, which deeply inspired this design.\n\nhttps://www.anthropic.com/engineering/built-multi-agent-research-system"
},
"label": "Note",
- "name": "Multi-Agent Deep Research"
+ "name": "Multi-Agent Deep research"
},
"dragHandle": ".note-drag-handle",
"dragging": false,
@@ -722,7 +722,7 @@
"text": "Choose a SOTA model with strong reasoning capabilities."
},
"label": "Note",
- "name": "Deep Research Lead Agent"
+ "name": "Deep research lead Agent"
},
"dragHandle": ".note-drag-handle",
"dragging": false,
diff --git a/agent/templates/deep_search_r.json b/agent/templates/deep_search_r.json
index 268b823577..0cd897dd80 100644
--- a/agent/templates/deep_search_r.json
+++ b/agent/templates/deep_search_r.json
@@ -2,13 +2,13 @@
{
"id": 6,
"title": {
- "en": "Deep Research",
+ "en": "Deep research",
"de": "Tiefgehende Recherche",
- "zh": "深度研究"},
+ "zh": "Deep research"},
"description": {
- "en": "For professionals in sales, marketing, policy, or consulting, the Multi-Agent Deep Research Agent conducts structured, multi-step investigations across diverse sources and delivers consulting-style reports with clear citations.",
+ "en": "For professionals in sales, marketing, policy, or consulting, the Multi-Agent Deep research Agent conducts structured, multi-step investigations across diverse sources and delivers consulting-style reports with clear citations.",
"de": "Für Fachleute in Vertrieb, Marketing, Politik oder Beratung führt der Multi-Agenten-Tiefenforschungsagent strukturierte, mehrstufige Untersuchungen über verschiedene Quellen durch und liefert Berichte im Beratungsstil mit klaren Quellenangaben.",
- "zh": "专为销售、市场、政策或咨询领域的专业人士设计，多智能体的深度研究会结合多源信息进行结构化、多步骤地回答问题，并附带有清晰的引用。"},
+ "zh": "专为销售、市场、政策或咨询领域的专业人士设计，多智能体的 Deep research 会结合多源信息进行结构化、多步骤地回答问题，并附带有清晰的引用。"},
"canvas_type": "Agent",
"dsl": {
"components": {
@@ -431,7 +431,7 @@
"visual_files_var": ""
},
"label": "Agent",
- "name": "Deep Research Agent"
+ "name": "Deep research Agent"
},
"dragging": false,
"id": "Agent:NewPumasLick",
@@ -692,10 +692,10 @@
{
"data": {
"form": {
- "text": "A Deep Research Agent built on a multi-agent architecture.\nMuch of the credit goes to Anthropic\u2019s blog post, which deeply inspired this design.\n\nhttps://www.anthropic.com/engineering/built-multi-agent-research-system"
+ "text": "A Deep research Agentic workflow built on a multi-agent architecture.\nMuch of the credit goes to Anthropic\u2019s blog post, which deeply inspired this design.\n\nhttps://www.anthropic.com/engineering/built-multi-agent-research-system"
},
"label": "Note",
- "name": "Multi-Agent Deep Research"
+ "name": "Multi-Agent Deep research"
},
"dragHandle": ".note-drag-handle",
"dragging": false,
@@ -722,7 +722,7 @@
"text": "Choose a SOTA model with strong reasoning capabilities."
},
"label": "Note",
- "name": "Deep Research Lead Agent"
+ "name": "Deep research lead Agent"
},
"dragHandle": ".note-drag-handle",
"dragging": false,
diff --git a/agent/templates/market_generate_seo_blog.json b/agent/templates/market_seo_article_writer.json
similarity index 98%
rename from agent/templates/market_generate_seo_blog.json
rename to agent/templates/market_seo_article_writer.json
index f230efdba7..0309036e14 100644
--- a/agent/templates/market_generate_seo_blog.json
+++ b/agent/templates/market_seo_article_writer.json
@@ -1,13 +1,13 @@
{
"id": 12,
"title": {
- "en": "Generate SEO Blog",
- "de": "SEO Blog generieren",
- "zh": "生成SEO博客"},
+ "en": "SEO article writer",
+ "de": "SEO-Blog-Magnetiseur",
+ "zh": "SEO 博客写手"},
"description": {
- "en": "This workflow automatically generates a complete SEO-optimized blog article based on a simple user input. You don't need any writing experience. Just provide a topic or short request — the system will handle the rest.",
- "de": "Dieser Workflow generiert automatisch einen vollständigen SEO-optimierten Blogartikel basierend auf einer einfachen Benutzereingabe. Sie benötigen keine Schreiberfahrung. Geben Sie einfach ein Thema oder eine kurze Anfrage ein – das System übernimmt den Rest.",
- "zh": "此工作流根据简单的用户输入自动生成完整的SEO博客文章。你无需任何写作经验，只需提供一个主题或简短请求，系统将处理其余部分。"},
+ "en": "This SEO article writer automatically generates a complete SEO-optimized blog article based on a simple user input. You don't need any writing experience. Just provide a topic or short request — the system will handle the rest.",
+ "de": "SEO-Blog-Magnetiseur automatisch einen vollständigen SEO-optimierten Blogartikel basierend auf einer einfachen Benutzereingabe. Sie benötigen keine Schreiberfahrung. Geben Sie einfach ein Thema oder eine kurze Anfrage ein – das System übernimmt den Rest.",
+ "zh": "此 SEO 博客写手根据简单的用户输入自动生成完整的SEO博客文章。你无需任何写作经验，只需提供一个主题或简短请求，系统将处理其余部分。"},
"canvas_type": "Marketing",
"dsl": {
"components": {
diff --git a/agent/templates/image_lingo.json b/agent/templates/photo_text_translator.json
similarity index 96%
rename from agent/templates/image_lingo.json
rename to agent/templates/photo_text_translator.json
index 2fba05b848..fcb6d71b30 100644
--- a/agent/templates/image_lingo.json
+++ b/agent/templates/photo_text_translator.json
@@ -1,13 +1,13 @@
{
"id": 13,
"title": {
- "en": "ImageLingo",
- "de": "ImageLingo",
- "zh": "图片解析"},
+ "en": "Photo text translator",
+ "de": "Bild-Dolmetscher",
+ "zh": "图片文字快译"},
"description": {
- "en": "ImageLingo lets you snap any photo containing text—menus, signs, or documents—and instantly recognize and translate it into your language of choice using advanced AI-powered translation technology.",
- "de": "ImageLingo ermöglicht es Ihnen, jedes Foto mit Text – Menüs, Schilder oder Dokumente – zu fotografieren und es sofort in Ihre gewünschte Sprache zu erkennen und zu übersetzen, unter Verwendung fortschrittlicher KI-gestützter Übersetzungstechnologie.",
- "zh": "多模态大模型允许您拍摄任何包含文本的照片——菜单、标志或文档——立即识别并转换成您选择的语言。"},
+ "en": "Photo text translator lets you snap any photo containing text—menus, signs, or documents—and instantly recognize and translate it into your language of choice using advanced AI-powered translation technology.",
+ "de": "Bild-Dolmetscher ermöglicht es Ihnen, jedes Foto mit Text – Menüs, Schilder oder Dokumente – zu fotografieren und es sofort in Ihre gewünschte Sprache zu erkennen und zu übersetzen, unter Verwendung fortschrittlicher KI-gestützter Übersetzungstechnologie.",
+ "zh": "图片文字快译允许您拍摄任何包含文本的照片——菜单、标志或文档——立即识别并转换成您选择的语言。"},
"canvas_type": "Consumer App",
"dsl": {
"components": {
diff --git a/agent/templates/knowledge_base_report.json b/agent/templates/reflective_academic_paper_generator.json
similarity index 97%
rename from agent/templates/knowledge_base_report.json
rename to agent/templates/reflective_academic_paper_generator.json
index 38cfb71589..bfb3e51e18 100644
--- a/agent/templates/knowledge_base_report.json
+++ b/agent/templates/reflective_academic_paper_generator.json
@@ -1,13 +1,13 @@
{
"id": 20,
"title": {
- "en": "Report Agent Using Knowledge Base",
- "de": "Berichtsagent mit Wissensdatenbank",
- "zh": "知识库检索智能体"},
+ "en": "Reflective academic paper generator",
+ "de": "Schreibhilfe für Reflexionspapiere",
+ "zh": "学术论文生成助手"},
"description": {
- "en": "A report generation assistant using local knowledge base, with advanced capabilities in task planning, reasoning, and reflective analysis. Recommended for academic research paper Q&A",
+ "en": "A reflective academic paper generator using local knowledge base, with advanced capabilities in task planning, reasoning, and reflective analysis. Recommended for academic research paper Q&A",
"de": "Ein Berichtsgenerierungsassistent, der eine lokale Wissensdatenbank nutzt, mit erweiterten Fähigkeiten in Aufgabenplanung, Schlussfolgerung und reflektierender Analyse. Empfohlen für akademische Forschungspapier-Fragen und -Antworten.",
- "zh": "一个使用本地知识库的报告生成助手，具备高级能力，包括任务规划、推理和反思性分析。推荐用于学术研究论文问答。"},
+ "zh": "一个使用本地知识库的学术论文生成助手，具备高级能力，包括任务规划、推理和反思性分析。推荐用于学术研究论文问答。"},
"canvas_type": "Agent",
"dsl": {
"components": {
diff --git a/agent/templates/knowledge_base_report_r.json b/agent/templates/reflective_academic_paper_generator_r.json
similarity index 97%
rename from agent/templates/knowledge_base_report_r.json
rename to agent/templates/reflective_academic_paper_generator_r.json
index 074250e6a8..1ff9afe59c 100644
--- a/agent/templates/knowledge_base_report_r.json
+++ b/agent/templates/reflective_academic_paper_generator_r.json
@@ -1,13 +1,13 @@
{
"id": 21,
"title": {
- "en": "Report Agent Using Knowledge Base",
- "de": "Berichtsagent mit Wissensdatenbank",
- "zh": "知识库检索智能体"},
+ "en": "Reflective academic paper generator",
+ "de": "Schreibhilfe für Reflexionspapiere",
+ "zh": "学术论文生成助手"},
"description": {
- "en": "A report generation assistant using local knowledge base, with advanced capabilities in task planning, reasoning, and reflective analysis. Recommended for academic research paper Q&A",
+ "en": "A reflective academic paper generator using local knowledge base, with advanced capabilities in task planning, reasoning, and reflective analysis. Recommended for academic research paper Q&A",
"de": "Ein Berichtsgenerierungsassistent, der eine lokale Wissensdatenbank nutzt, mit erweiterten Fähigkeiten in Aufgabenplanung, Schlussfolgerung und reflektierender Analyse. Empfohlen für akademische Forschungspapier-Fragen und -Antworten.",
- "zh": "一个使用本地知识库的报告生成助手，具备高级能力，包括任务规划、推理和反思性分析。推荐用于学术研究论文问答。"},
+ "zh": "一个使用本地知识库的学术论文生成助手，具备高级能力，包括任务规划、推理和反思性分析。推荐用于学术研究论文问答。"},
"canvas_type": "Recommended",
"dsl": {
"components": {
diff --git a/agent/templates/generate_SEO_blog.json b/agent/templates/seo_article_writer.json
similarity index 99%
rename from agent/templates/generate_SEO_blog.json
rename to agent/templates/seo_article_writer.json
index 33a656246c..3169f31fd6 100644
--- a/agent/templates/generate_SEO_blog.json
+++ b/agent/templates/seo_article_writer.json
@@ -1,13 +1,13 @@
{
"id": 8,
"title": {
- "en": "Generate SEO Blog",
- "de": "SEO Blog generieren",
- "zh": "生成SEO博客"},
+ "en": "SEO article writer",
+ "de": "SEO-Blog-Magnetiseur",
+ "zh": "SEO 博客写手"},
"description": {
"en": "This is a multi-agent version of the SEO blog generation workflow. It simulates a small team of AI “writers”, where each agent plays a specialized role — just like a real editorial team.",
"de": "Dies ist eine Multi-Agenten-Version des Workflows zur Erstellung von SEO-Blogs. Sie simuliert ein kleines Team von KI-„Autoren“, in dem jeder Agent eine spezielle Rolle übernimmt – genau wie in einem echten Redaktionsteam.",
- "zh": "多智能体架构可根据简单的用户输入自动生成完整的SEO博客文章。模拟小型“作家”团队，其中每个智能体扮演一个专业角色——就像真正的编辑团队。"},
+ "zh": "SEO 博客写手可根据简单的用户输入自动生成完整的SEO博客文章。模拟小型“作家”团队，其中每个智能体扮演一个专业角色——就像真正的编辑团队。"},
"canvas_type": "Agent",
"dsl": {
"components": {
diff --git a/agent/templates/seo_blog.json b/agent/templates/seo_article_writer_r.json
similarity index 98%
rename from agent/templates/seo_blog.json
rename to agent/templates/seo_article_writer_r.json
index e06c28f0cc..a49c4c6882 100644
--- a/agent/templates/seo_blog.json
+++ b/agent/templates/seo_article_writer_r.json
@@ -1,13 +1,13 @@
{
"id": 4,
"title": {
- "en": "Generate SEO Blog",
- "de": "SEO Blog generieren",
- "zh": "生成SEO博客"},
+ "en": "SEO article writer",
+ "de": "SEO-Blog-Magnetiseur",
+ "zh": "SEO 博客写手"},
"description": {
- "en": "This workflow automatically generates a complete SEO-optimized blog article based on a simple user input. You don't need any writing experience. Just provide a topic or short request — the system will handle the rest.",
- "de": "Dieser Workflow generiert automatisch einen vollständigen SEO-optimierten Blogartikel basierend auf einer einfachen Benutzereingabe. Sie benötigen keine Schreiberfahrung. Geben Sie einfach ein Thema oder eine kurze Anfrage ein – das System übernimmt den Rest.",
- "zh": "此工作流根据简单的用户输入自动生成完整的SEO博客文章。你无需任何写作经验，只需提供一个主题或简短请求，系统将处理其余部分。"},
+ "en": "This SEO article writer automatically generates a complete SEO-optimized blog article based on a simple user input. You don't need any writing experience. Just provide a topic or short request — the system will handle the rest.",
+ "de": "SEO-Blog-Magnetiseur automatisch einen vollständigen SEO-optimierten Blogartikel basierend auf einer einfachen Benutzereingabe. Sie benötigen keine Schreiberfahrung. Geben Sie einfach ein Thema oder eine kurze Anfrage ein – das System übernimmt den Rest.",
+ "zh": "此 SEO 博客写手根据简单的用户输入自动生成完整的SEO博客文章。你无需任何写作经验，只需提供一个主题或简短请求，系统将处理其余部分。"},
"canvas_type": "Recommended",
"dsl": {
"components": {
diff --git a/agent/templates/ecommerce_customer_service_workflow.json b/agent/templates/smart_customer_service_specialist.json
similarity index 98%
rename from agent/templates/ecommerce_customer_service_workflow.json
rename to agent/templates/smart_customer_service_specialist.json
index a56c0a547c..a4d656b365 100644
--- a/agent/templates/ecommerce_customer_service_workflow.json
+++ b/agent/templates/smart_customer_service_specialist.json
@@ -1,14 +1,14 @@
{
"id": 22,
"title": {
- "en": "Ecommerce Customer Service Workflow",
+ "en": "Smart customer service specialist",
"de": "Ecommerce Kundenservice Workflow",
- "zh": "电子商务客户服务工作流程"
+ "zh": "智能客户服务专员"
},
"description": {
- "en": "This template helps e-commerce platforms address complex customer needs, such as comparing product features, providing usage support, and coordinating home installation services.",
- "de": "Diese Vorlage hilft E-Commerce-Plattformen, komplexe Kundenbedürfnisse zu erfüllen, wie z.B. den Vergleich von Produktmerkmalen, die Bereitstellung von Nutzungsunterstützung und die Koordination von Hausinstallationsdiensten.",
- "zh": "该模板可帮助电子商务平台解决复杂的客户需求，例如比较产品功能、提供使用支持和协调家庭安装服务。"
+ "en": "This template helps address complex customer needs, such as comparing product features, providing usage support, and coordinating home installation services.",
+ "de": "Diese Vorlage hilft komplexe Kundenbedürfnisse zu erfüllen, wie z.B. den Vergleich von Produktmerkmalen, die Bereitstellung von Nutzungsunterstützung und die Koordination von Hausinstallationsdiensten.",
+ "zh": "该模板可帮助解决复杂的客户需求，例如比较产品功能、提供使用支持和协调家庭安装服务。"
},
"canvas_type": "Customer Support",
"dsl": {
diff --git a/agent/templates/stock_research_report.json b/agent/templates/stock_market_research_assistant.json
similarity index 98%
rename from agent/templates/stock_research_report.json
rename to agent/templates/stock_market_research_assistant.json
index 786d5adbcc..00e9cecd4a 100644
--- a/agent/templates/stock_research_report.json
+++ b/agent/templates/stock_market_research_assistant.json
@@ -1,1173 +1,1173 @@
-{
- "id": 26,
- "title": {
- "en": "Stock Research Report Agent",
- "de": "Aktienanalyse Agent",
- "zh": "股票研究报告智能体"
- },
- "description": {
- "en": "This template helps financial analysts quickly organize information — it can automatically retrieve company data, consolidate financial metrics, and integrate research report insights.",
- "de": "Diese Vorlage hilft Finanzanalysten, Informationen schnell zu organisieren – der Agent kann automatisch Unternehmensdaten abrufen, Finanzkennzahlen konsolidieren und Forschungsberichte integrieren.",
- "zh": "这个模板可以帮助金融分析师快速整理信息——它能够自动获取公司数据、整合财务指标，并汇总研报观点。"
- },
- "canvas_type": "Recommended",
- "dsl": {
- "components": {
- "Agent:ManyToesBrush": {
- "downstream": [
- "Switch:FluffyCoinsSell"
- ],
- "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-latest@Tongyi-Qianwen",
- "maxTokensEnabled": false,
- "max_retries": 3,
- "max_rounds": 1,
- "max_tokens": 256,
- "mcp": [],
- "message_history_window_size": 12,
- "outputs": {
- "content": {
- "type": "string",
- "value": ""
- }
- },
- "presencePenaltyEnabled": false,
- "presence_penalty": 0.4,
- "prompts": [
- {
- "content": "{sys.query}",
- "role": "user"
- }
- ],
- "sys_prompt": " \n\nYour responsibility is to identify and extract the stock name or abbreviation from the user's natural language query and return the corresponding unique stock code.\n\n \n\n\n\n \n\n1. Only one result is allowed: - If a stock is identified \u2192 only return the corresponding stock code; - If no stock is identified \u2192 only return \u201cNot Found\u201d. 2. **Do not** output any additional text, punctuation, explanation, prefixes, or line breaks. 3. The output must strictly adhere to the . \n\n\n\n\n\nOnly output the stock code (e.g., AAPL or 600519)\nOr only output \u201cNot Found\u201d\n\n\n\n\nUser input: \u201cHelp me check the research report of Apple\u201d \u2192 Output: AAPL\nUser input: \u201cHow is Maotai\u2019s financial performance\u201d \u2192 Output: 600519\nUser input: \u201cHow is the Shanghai Composite Index doing today\u201d \u2192 Output: Not Found\n\n\n\n - Tavily Search: Use this tool when you are unsure of the stock code. - If you are confident, you do not need to use the tool. \n\n\n\n\n\nOnly output the result, no explanations, hints, or notes allowed.\nThe output can only be the stock code or \u201cNot Found\u201d, otherwise, it is considered an incorrect answer.\n",
- "temperature": 0.1,
- "temperatureEnabled": false,
- "tools": [
- {
- "component_name": "TavilySearch",
- "name": "TavilySearch",
- "params": {
- "api_key": "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",
- "days": 7,
- "exclude_domains": [],
- "include_answer": false,
- "include_domains": [],
- "include_image_descriptions": false,
- "include_images": false,
- "include_raw_content": true,
- "max_results": 5,
- "outputs": {
- "formalized_content": {
- "type": "string",
- "value": ""
- },
- "json": {
- "type": "Array