Dify Chatflow: 数字能量解读工作流

Overview

A Dify Chatflow that accepts user phone numbers + questions, assembles chart context, and generates AI readings.

Workflow Design

Nodes

1. Start Node (Input)

query: string — user's natural language question (e.g., "我的职业发展如何？")
phone: string — 11-digit phone number
chart_data: json — pre-calculated triangle data from mini-program (if provided)
user_id: string — WeChat OpenID

2. Python Node: `context_builder`

Node ID: context_builder
Purpose: Assemble chart data into structured context for LLM

def main(query: str, phone: str, chart_data: dict, user_id: str) -> dict:
    import json

    # Build position descriptions
    positions = {
        "A": "命宫/事业", "B": "福德宫", "C": "父母宫",
        "D": "迁移宫", "E": "财帛宫/财富",
        "F": "夫妻宫/感情", "G": "兄弟宫", "N": "交友宫",
        "H": "官禄宫", "I": "田宅宫",
        "J": "疾厄宫", "M": "子女宫", "L": "仆役宫",
        "K": "财库", "O": "整体/综合"
    }

    if chart_data:
        context = "## 数字能量分析数据\n\n"
        context += "### 各宫位数字\n\n"
        context += "| 宫位 | 数字 | 含义 |\n"
        context += "|------|------|------|\n"

        for key, label in positions.items():
            num = chart_data.get(key, "—")
            context += f"| {key}({label}) | {num} | |\n"

        # Add primary positions
        primary = ["A", "E", "F", "O"]
        context += "\n### 主星能量\n\n"
        for p in primary:
            if p in chart_data:
                context += f"- {p}({positions[p]}): {chart_data[p]}\n"

        # Add combinations
        pairs = [("A","E"),("A","F"),("E","O"),("F","O"),("A","O")]
        context += "\n### 重要数字组合\n\n"
        for a, b in pairs:
            if a in chart_data and b in chart_data:
                context += f"- {a}({chart_data[a]}) + {b}({chart_data[b]}) = {(chart_data[a]+chart_data[b])%10}\n"

        context += "\n### 手机号\n"
        context += f"- {phone}\n"
    else:
        context = "未提供图表数据。"

    # Identify question intent
    intents = {
        "职业": "career", "事业": "career", "工作": "career",
        "财运": "wealth", "财": "wealth", "赚钱": "wealth",
        "感情": "relationship", "爱情": "relationship", "婚姻": "relationship",
        "健康": "health", "身体": "health",
        "家庭": "family", "家人": "family"
    }

    intent = "general"
    for keyword, it in intents.items():
        if keyword in query:
            intent = it
            break

    return {
        "context": context,
        "intent": intent,
        "formatted": f"用户问题：{query}\n\n分析焦点：{intent}\n\n{context}"
    }

3. Knowledge Retrieval Node (Optional)

Retrieves from Digital Energy knowledge base if configured.

4. LLM Node: `reading_generator`

Model: GPT-4o / Claude 3.5 Sonnet
Context: System prompt + context from Python node
Temperature: 0.7

System Prompt:

你是资深数字能量学专家。你的任务是基于用户手机号的数字能量三角图数据，提供专业、准确、有价值的解读。

## 核心原则
1. 只基于用户提供的数字进行分析，不凭空编造
2. 保持客观，不制造焦虑或过度承诺
3. 分析应该既有专业术语又通俗易懂
4. 给出建设性建议而非宿命论结论

## 解读框架
1. 主星分析：A(事业)、E(财富)、F(感情)、O(综合)
2. 数字组合：关键配对的含义
3. 具体领域分析：根据用户问题聚焦（事业/财富/感情/健康/家庭）
4. 建议和指引

## 重要格式
- 使用自然流畅的中文
- 适当分段，有层次感
- 重点数字用加粗或引号标注

User Prompt Template:

{{formatted}}

5. End Node (Output)

answer: string — AI response text
intent: string — detected intent category

Dify API Integration

Endpoint

POST /v1/workflows/run
Authorization: Bearer {api_key}
Content-Type: application/json

Request Body

{
  "inputs": {
    "query": "我的事业运势如何？",
    "phone": "13800138000",
    "chart_data": {"A": 3, "B": 5, ...},
    "user_id": "wx_user_001"
  },
  "response_mode": "blocking",
  "user": "wx_user_001"
}

Response

{
  "workflow_run_id": "run_xxx",
  "data": {
    "outputs": {
      "answer": "根据您的数字能量分析...",
      "intent": "career"
    },
    "status": "succeeded"
  }
}

Backend Integration (Java)

See DifyService.java for Spring Boot client implementation.

Deployment

Create a new Chatflow in Dify console
Add nodes in order: Start → Python → LLM → End
Copy the Python code into the Python node
Configure LLM node with system prompt above
Set Start node input schema
Deploy as API service
Copy API key to application.yml

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