ShedBoxAI for E-commerce

data_sources:
  orders:
    type: rest
    url: "https://${SHOPIFY_STORE}.myshopify.com/admin/api/2024-01/orders.json"
    headers:
      X-Shopify-Access-Token: "${SHOPIFY_ACCESS_TOKEN}"
    response_path: "orders"

  payments:
    type: rest
    url: "https://api.stripe.com/v1/charges"
    headers:
      Authorization: "Bearer ${STRIPE_SECRET_KEY}"
    response_path: "data"

processing:
  relationship_highlighting:
    orders:
      link_fields:
        - source: "orders"
          source_field: "email"
          to: "payments"
          target_field: "receipt_email"

  advanced_operations:
    customer_metrics:
      source: "orders"
      group_by: "customer.id"
      aggregate:
        total_orders: "COUNT(*)"
        lifetime_value: "SUM(total_price)"
        avg_order_value: "AVG(total_price)"
      sort: "-lifetime_value"

ai_interface:
  model:
    type: rest
    url: "https://api.anthropic.com/v1/messages"
    method: POST
    headers:
      x-api-key: "${ANTHROPIC_API_KEY}"
      Content-Type: "application/json"
    options:
      model: "claude-sonnet-4-20250514"

  prompts:
    segment:
      system: "You are a customer segmentation expert."
      user_template: |
        Segment these customers based on their purchase behavior:

        {% for customer in customer_metrics %}
        - Customer {{ customer.customer_id }}: {{ customer.total_orders }} orders, ${{ customer.lifetime_value }} LTV
        {% endfor %}

        Provide segmentation recommendations.

output:
  type: file
  path: "customer_segments.json"
  format: json

data_sources:
  inventory:
    type: csv
    path: "inventory_export.csv"

processing:
  contextual_filtering:
    inventory:
      - field: "quantity"
        condition: "<10"
        new_name: "low_stock_items"

  advanced_operations:
    sorted_stock:
      source: "low_stock_items"
      sort: "quantity"

output:
  type: file
  path: "low_stock_alerts.json"
  format: json

data_sources:
  historical_sales:
    type: csv
    path: "sales_2024.csv"

processing:
  content_summarization:
    historical_sales:
      method: "statistical"
      fields: ["revenue", "units_sold"]
      summarize: ["sum", "mean", "min", "max"]

ai_interface:
  model:
    type: rest
    url: "https://api.anthropic.com/v1/messages"
    method: POST
    headers:
      x-api-key: "${ANTHROPIC_API_KEY}"
      Content-Type: "application/json"
    options:
      model: "claude-sonnet-4-20250514"

  prompts:
    forecast:
      system: "You are a sales forecasting analyst."
      user_template: |
        Based on this historical sales data, forecast next month's sales:

        {% for month in historical_sales %}
        - {{ month.month }}: ${{ month.revenue }} revenue, {{ month.units_sold }} units
        {% endfor %}

        Provide forecasts with reasoning.

output:
  type: file
  path: "sales_forecast.md"
  format: json