hankin
423baff73b
- Docker bridge 网络隔离（8000 端口封死） - Gunicorn 4 Worker 多进程 - Alembic 数据库迁移基线 - 日志轮转 20m×3 - JWT 密钥 + DB 密码 + CORS 收紧 - 3-2-1 备份链路（NAS + R740-B 冷备） - 连接池 pool_pre_ping + pool_recycle=3600
177 lines
5.1 KiB
YAML
177 lines
5.1 KiB
YAML
app:
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description: '接收销售日志内容，AI 提取客户画像特征（痛点/偏好/购买意向），通过 HTTP 回写至 ERP 客户表的 ai_persona 字段'
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icon: "\U0001F4DD"
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icon_background: '#E4FBCC'
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mode: workflow
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name: '销售日志 → 客户画像提取'
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use_icon_as_answer_icon: false
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kind: app
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version: 0.1.3
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workflow:
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conversation_variables: []
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environment_variables: []
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features:
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file_upload:
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image:
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enabled: false
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opening_statement: ''
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retriever_resource:
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enabled: false
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sensitive_word_avoidance:
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enabled: false
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suggested_questions: []
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suggested_questions_after_answer:
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enabled: false
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text_to_speech:
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enabled: false
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graph:
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edges:
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- data:
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sourceType: start
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targetType: llm
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id: edge-start-to-llm
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source: start
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target: llm-extract
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type: custom
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- data:
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sourceType: llm
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targetType: http-request
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id: edge-llm-to-http
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source: llm-extract
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target: http-update-persona
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type: custom
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- data:
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sourceType: http-request
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targetType: end
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id: edge-http-to-end
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source: http-update-persona
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target: end
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type: custom
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nodes:
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# ── 开始节点 ──
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- data:
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desc: '接收销售日志参数'
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title: 开始
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type: start
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variables:
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- label: customer_id
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max_length: 100
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required: true
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type: text-input
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variable: customer_id
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- label: content
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max_length: 5000
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required: true
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type: paragraph
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variable: content
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- label: salesperson_name
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max_length: 50
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required: false
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type: text-input
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variable: salesperson_name
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height: 90
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id: start
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position:
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x: 80
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y: 282
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type: start
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width: 244
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# ── LLM 画像提取节点 ──
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- data:
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desc: '从销售日志中提取客户画像特征'
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# ⚠️ 导入后请在此节点选择你 Dify 中已配置的模型
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# 推荐选择 Qwen3.5-27B (R740-A 节点)
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model:
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completion_params:
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temperature: 0.3
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max_tokens: 1024
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mode: chat
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name: qwen3.5 # ← 导入后改为你实际的模型名称
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provider: ollama # ← 改为你的模型供应商
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prompt_template:
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- role: system
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text: |
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你是一名 B2B 润滑油行业的客户分析专家。
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根据销售人员的日志内容，提取客户特征并输出严格的 JSON 格式。
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输出要求（纯 JSON，不要 markdown 代码块）：
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{
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"pain_points": ["痛点1", "痛点2"],
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"preferences": ["偏好1", "偏好2"],
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"purchase_intent": "high 或 medium 或 low",
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"key_decision_makers": ["决策人姓名"],
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"competitor_info": ["竞品信息"],
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"summary": "一句话总结本次跟进要点"
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}
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如果某个字段从日志中无法提取，返回空数组 [] 或 "unknown"。
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- role: user
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text: |
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销售人员: {{#start.salesperson_name#}}
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日志内容:
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{{#start.content#}}
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title: 'AI 画像提取'
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type: llm
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height: 98
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id: llm-extract
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position:
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x: 380
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y: 282
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type: llm
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width: 244
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# ── HTTP 回写客户画像 ──
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- data:
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desc: '将提取的画像 JSON 写入 ERP 客户表'
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title: '回写客户画像'
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type: http-request
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authorization:
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config:
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api_key: '' # ← 导入后填写 Bearer token
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header: Authorization
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type: bearer
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type: api-key
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body:
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data: '{{#llm-extract.text#}}'
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type: json
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headers: ''
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method: put
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params: ''
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timeout:
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max_connect_timeout: 10
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max_read_timeout: 30
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max_write_timeout: 30
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# ⚠️ 导入后修改为你的实际 ERP 地址
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url: 'http://192.168.1.100:8000/api/customers/{{#start.customer_id#}}/persona'
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height: 98
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id: http-update-persona
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position:
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x: 680
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y: 282
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type: http-request
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width: 244
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# ── 结束节点 ──
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- data:
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desc: ''
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outputs:
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- value_selector:
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- llm-extract
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- text
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variable: ai_persona_result
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- value_selector:
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- http-update-persona
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- status_code
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variable: http_status
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title: 结束
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type: end
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height: 90
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id: end
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position:
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x: 980
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y: 282
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type: end
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width: 244
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hash: ''
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