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219
api/app/templates/workflows/customer_service/template.yml
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219
api/app/templates/workflows/customer_service/template.yml
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# 智能客服工作流模板
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id: customer_service_v1
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name: 智能客服工作流
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description: 智能客服场景，包含意图识别、知识库查询和回复生成
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category: customer_service
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version: "1.0.0"
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author: RedBear Memory Team
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tags:
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- 客服
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- 意图识别
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- 知识库
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- 多步骤
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# 工作流配置
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nodes:
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- id: start
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type: start
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name: 开始
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position:
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x: 100
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y: 200
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- id: intent_recognition
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type: llm
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name: 意图识别
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config:
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prompt: |
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分析用户的问题，识别意图类型。
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用户问题：{{ var.user_message }}
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请从以下类型中选择一个：
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- product_inquiry: 产品咨询
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- technical_support: 技术支持
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- complaint: 投诉建议
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- other: 其他
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只返回类型名称，不要其他内容。
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model: gpt-3.5-turbo
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temperature: 0.3
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max_tokens: 50
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position:
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x: 300
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y: 200
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- id: intent_router
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type: condition
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name: 意图路由
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position:
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x: 500
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y: 200
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- id: product_handler
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type: llm
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name: 产品咨询处理
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config:
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prompt: |
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用户咨询产品相关问题。
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问题：{{ var.user_message }}
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意图：{{ node.intent_recognition.output }}
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请提供专业、友好的产品咨询回复。
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model: gpt-3.5-turbo
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temperature: 0.7
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max_tokens: 500
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position:
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x: 700
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y: 100
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- id: support_handler
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type: llm
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name: 技术支持处理
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config:
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prompt: |
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用户需要技术支持。
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问题：{{ var.user_message }}
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意图：{{ node.intent_recognition.output }}
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请提供详细的技术支持方案。
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model: gpt-3.5-turbo
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temperature: 0.5
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max_tokens: 800
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position:
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x: 700
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y: 200
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- id: complaint_handler
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type: llm
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name: 投诉处理
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config:
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prompt: |
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用户提出投诉或建议。
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问题：{{ var.user_message }}
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意图：{{ node.intent_recognition.output }}
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请以同理心回应，并提供解决方案。
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model: gpt-3.5-turbo
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temperature: 0.8
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max_tokens: 600
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position:
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x: 700
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y: 300
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- id: general_handler
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type: llm
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name: 通用处理
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config:
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prompt: |
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用户的问题类型：其他
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问题：{{ var.user_message }}
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请提供友好的回复。
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model: gpt-3.5-turbo
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temperature: 0.7
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max_tokens: 400
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position:
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x: 700
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y: 400
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- id: end
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type: end
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name: 结束
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position:
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x: 900
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y: 200
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edges:
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- source: start
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target: intent_recognition
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label: 开始分析
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- source: intent_recognition
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target: intent_router
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label: 识别完成
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- source: intent_router
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target: product_handler
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condition: "'product_inquiry' in node['intent_recognition']['output']"
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label: 产品咨询
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- source: intent_router
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target: support_handler
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condition: "'technical_support' in node['intent_recognition']['output']"
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label: 技术支持
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- source: intent_router
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target: complaint_handler
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condition: "'complaint' in node['intent_recognition']['output']"
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label: 投诉建议
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- source: intent_router
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target: general_handler
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condition: "True" # 默认路径
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label: 其他
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- source: product_handler
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target: end
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label: 完成
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- source: support_handler
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target: end
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label: 完成
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- source: complaint_handler
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target: end
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label: 完成
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- source: general_handler
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target: end
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label: 完成
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# 变量定义
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variables:
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- name: user_message
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type: string
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required: true
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description: 用户的消息
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default: ""
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- name: user_name
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type: string
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required: false
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description: 用户姓名（可选）
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default: "客户"
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# 执行配置
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execution_config:
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max_execution_time: 120
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max_iterations: 10
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# 触发器
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triggers: []
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# 使用示例
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examples:
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- name: 产品咨询
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description: 用户咨询产品功能
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input:
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user_message: "你们的产品支持多语言吗？"
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user_name: "张三"
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expected_output: "产品功能介绍"
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- name: 技术支持
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description: 用户遇到技术问题
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input:
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user_message: "我无法登录系统，一直显示密码错误"
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user_name: "李四"
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expected_output: "技术支持方案"
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- name: 投诉处理
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description: 用户提出投诉
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input:
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user_message: "你们的服务态度太差了，我要投诉"
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user_name: "王五"
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expected_output: "同理心回应和解决方案"
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131
api/app/templates/workflows/data_processing/template.yml
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131
api/app/templates/workflows/data_processing/template.yml
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# 数据处理工作流模板
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id: data_processing_v1
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name: 数据处理工作流
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description: 数据提取、转换和分析的完整流程
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category: data_processing
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version: "1.0.0"
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author: RedBear Memory Team
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tags:
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- 数据处理
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- ETL
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- 分析
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- Transform
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# 工作流配置
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nodes:
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- id: start
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type: start
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name: 开始
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position:
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x: 100
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y: 200
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- id: extract_data
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type: transform
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name: 数据提取
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config:
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expression: |
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{
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"raw_text": var['input_text'],
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"length": len(var['input_text']),
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"timestamp": sys['execution_id']
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}
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position:
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x: 300
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y: 200
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- id: analyze_data
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type: llm
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name: 数据分析
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config:
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prompt: |
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请分析以下数据：
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原始文本：{{ node.extract_data.raw_text }}
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文本长度：{{ node.extract_data.length }}
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请提供：
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1. 主题分类
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2. 情感分析
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3. 关键信息提取
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以 JSON 格式返回结果。
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model: gpt-3.5-turbo
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temperature: 0.3
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max_tokens: 500
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position:
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x: 500
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y: 200
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- id: transform_result
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type: transform
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name: 结果转换
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config:
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expression: |
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{
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"original_length": node['extract_data']['length'],
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"analysis": node['analyze_data']['output'],
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"processed_at": sys['execution_id'],
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"status": "completed"
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}
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position:
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x: 700
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y: 200
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- id: end
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type: end
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name: 结束
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position:
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x: 900
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y: 200
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edges:
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- source: start
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target: extract_data
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label: 开始提取
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- source: extract_data
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target: analyze_data
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label: 开始分析
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- source: analyze_data
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target: transform_result
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label: 转换结果
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- source: transform_result
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target: end
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label: 完成
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# 变量定义
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variables:
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- name: input_text
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type: string
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required: true
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description: 待处理的文本数据
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default: ""
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# 执行配置
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execution_config:
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max_execution_time: 180
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max_iterations: 5
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# 触发器
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triggers: []
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# 使用示例
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examples:
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- name: 文本分析
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description: 分析一段文本
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input:
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input_text: "今天天气真好，心情也很愉快。我们公司推出了新产品，市场反响热烈。"
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expected_output:
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original_length: 35
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analysis: "主题：天气和产品，情感：积极"
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status: "completed"
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- name: 长文本处理
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description: 处理较长的文本
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input:
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input_text: "这是一段很长的文本..."
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expected_output:
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status: "completed"
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99
api/app/templates/workflows/multi_step_qa/template.yml
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99
api/app/templates/workflows/multi_step_qa/template.yml
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# 多步骤问答工作流
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# 演示节点输出参数的使用
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id: multi_step_qa_v1
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name: 多步骤问答工作流
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description: 先分析问题，再生成答案，展示节点间的数据传递
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category: advanced
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version: "1.0.0"
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author: RedBear Memory Team
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tags:
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- 问答
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- 多步骤
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- LLM
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# 工作流配置
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nodes:
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- id: start
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type: start
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name: 开始
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position:
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x: 100
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y: 100
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- id: analyze_question
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type: llm
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name: 分析问题
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description: 分析用户问题的类型和意图
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config:
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model_id: gpt-3.5-turbo
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temperature: 0.3
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max_tokens: 500
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messages:
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- role: system
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content: |
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你是一个问题分析专家。请分析用户的问题，提取以下信息：
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1. 问题类型（事实性、观点性、操作性等）
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2. 问题领域（科技、历史、文化等）
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3. 关键词
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- role: user
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content: "{{ sys.message }}"
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position:
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x: 300
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y: 100
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- id: generate_answer
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type: llm
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name: 生成答案
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description: 根据问题分析结果生成详细答案
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config:
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model_id: gpt-3.5-turbo
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temperature: 0.7
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max_tokens: 1000
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messages:
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- role: system
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content: |
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你是一个专业的AI助手。根据问题分析结果，生成准确、详细的答案。
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问题分析结果：
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{{ analyze_question.output }}
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- role: user
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content: "{{ sys.message }}"
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position:
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x: 500
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y: 100
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- id: end
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type: end
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name: 结束
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config:
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output: "{{ generate_answer.output }}"
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position:
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x: 700
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y: 100
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edges:
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- source: start
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target: analyze_question
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label: 开始分析
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- source: analyze_question
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target: generate_answer
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label: 生成答案
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- source: generate_answer
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target: end
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label: 完成
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# 变量定义
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variables:
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- name: user_question
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type: string
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required: true
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description: 用户的问题
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default: ""
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||||
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||||
# 执行配置
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||||
execution_config:
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max_execution_time: 120
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max_iterations: 1
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100
api/app/templates/workflows/simple_qa/template.yml
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100
api/app/templates/workflows/simple_qa/template.yml
Normal file
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# 简单问答工作流模板
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id: simple_qa_v1
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name: 简单问答工作流
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description: 最基础的问答工作流，适合快速开始
|
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category: basic
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||||
version: "1.0.0"
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author: RedBear Memory Team
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tags:
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- 问答
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- 基础
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- LLM
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# 工作流配置
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nodes:
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- id: start
|
||||
type: start
|
||||
name: 开始
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||||
position:
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||||
x: 100
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||||
y: 100
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||||
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- id: llm_qa
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type: llm
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name: LLM 问答
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config:
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# 使用 LangChain 标准的消息格式
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messages:
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||||
- role: system
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||||
content: |
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||||
你是一个专业、友好且乐于助人的 AI 助手。
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||||
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||||
你的职责：
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- 准确理解用户的问题并提供有价值的回答
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||||
- 保持回答的专业性和准确性
|
||||
- 如果不确定答案，诚实地告知用户
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- 使用清晰、易懂的语言进行交流
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||||
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||||
回答风格：
|
||||
- 简洁明了，直击要点
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||||
- 必要时提供详细解释和示例
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||||
- 使用友好、礼貌的语气
|
||||
- 适当使用格式化（如列表、段落）提高可读性
|
||||
|
||||
- role: user
|
||||
content: "{{ sys.message }}"
|
||||
|
||||
model_id: gpt-3.5-turbo
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||||
temperature: 0.7
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||||
max_tokens: 1000
|
||||
position:
|
||||
x: 300
|
||||
y: 100
|
||||
|
||||
- id: end
|
||||
type: end
|
||||
name: 结束
|
||||
config:
|
||||
output: "{{ llm_qa.output }}"
|
||||
position:
|
||||
x: 500
|
||||
y: 100
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||||
|
||||
edges:
|
||||
- source: start
|
||||
target: llm_qa
|
||||
label: 开始处理
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||||
|
||||
- source: llm_qa
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||||
target: end
|
||||
label: 完成
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||||
|
||||
# 变量定义
|
||||
variables:
|
||||
- name: user_question
|
||||
type: string
|
||||
required: true
|
||||
description: 用户的问题
|
||||
default: ""
|
||||
|
||||
# 执行配置
|
||||
execution_config:
|
||||
max_execution_time: 60
|
||||
max_iterations: 1
|
||||
|
||||
# 触发器（可选）
|
||||
triggers: []
|
||||
|
||||
# 使用示例
|
||||
examples:
|
||||
- name: 基础问答
|
||||
description: 询问一个简单的问题
|
||||
input:
|
||||
user_question: "什么是人工智能？"
|
||||
expected_output: "关于人工智能的解释"
|
||||
|
||||
- name: 技术咨询
|
||||
description: 询问技术问题
|
||||
input:
|
||||
user_question: "如何学习 Python 编程？"
|
||||
expected_output: "Python 学习建议"
|
||||
Reference in New Issue
Block a user