高于平均累计消费客户

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  "difficulty": "hard",
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    "columns": [
      {
        "name": "customer_id",
        "type": "BIGINT"
      },
      {
        "name": "customer_name",
        "type": "VARCHAR"
      },
      {
        "name": "total_spend",
        "type": "DECIMAL(38,2)"
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  "finished_at": "2026-08-29T22:07:54.592777",
  "formatted_sql": "SELECT\n  customer_id,\n  customer_name,\n  total_spend\nFROM (\n  SELECT\n    customer_spend.customer_id,\n    customer_spend.customer_name,\n    customer_spend.total_spend,\n    AVG(customer_spend.total_spend) OVER () AS avg_total_spend\n  FROM (\n    SELECT\n      c.customer_id,\n      c.customer_name,\n      SUM(\n        COALESCE(oi.quantity * oi.unit_price - oi.discount_amount, CAST(0 AS DECIMAL(14, 2)))\n      ) AS total_spend\n    FROM fact_orders AS o\n    INNER JOIN dim_customers AS c\n      ON o.customer_id = c.customer_id\n    LEFT JOIN fact_order_items AS oi\n      ON o.order_id = oi.order_id\n    WHERE\n      o.status = 'completed'\n    GROUP BY\n      c.customer_id,\n      c.customer_name\n  ) AS customer_spend\n) AS customer_spend_with_average\nWHERE\n  total_spend > avg_total_spend\nORDER BY\n  total_spend DESC,\n  customer_id ASC",
  "generated_sql": "SELECT\n    customer_id,\n    customer_name,\n    total_spend\nFROM (\n    SELECT\n        customer_spend.customer_id,\n        customer_spend.customer_name,\n        customer_spend.total_spend,\n        AVG(customer_spend.total_spend) OVER () AS avg_total_spend\n    FROM (\n        SELECT\n            c.customer_id,\n            c.customer_name,\n            SUM(\n                COALESCE(\n                    oi.quantity * oi.unit_price - oi.discount_amount,\n                    CAST(0 AS DECIMAL(14, 2))\n                )\n            ) AS total_spend\n        FROM fact_orders AS o\n        INNER JOIN dim_customers AS c\n            ON o.customer_id = c.customer_id\n        LEFT JOIN fact_order_items AS oi\n            ON o.order_id = oi.order_id\n        WHERE o.status = 'completed'\n        GROUP BY\n            c.customer_id,\n            c.customer_name\n    ) AS customer_spend\n) AS customer_spend_with_average\nWHERE total_spend > avg_total_spend\nORDER BY\n    total_spend DESC,\n    customer_id ASC",
  "generation_ms": null,
  "id": 117,
  "model_name": "GPT 当前会话桥接（流程验收）",
  "model_run_id": 19,
  "plan": {
    "filters": [
      "fact_orders.status = 'completed'",
      "total_spend > 有已完成订单客户的平均 total_spend"
    ],
    "grain": "每行一个高于基准的客户。",
    "joins": [
      "fact_orders.customer_id = dim_customers.customer_id",
      "fact_order_items.order_id = fact_orders.order_id（LEFT JOIN）"
    ],
    "metrics": [
      "total_spend = SUM(quantity * unit_price - discount_amount)",
      "平均累计消费 = AVG(customer-level total_spend)"
    ],
    "risks": [
      "先按客户聚合再计算平均，避免订单行数造成客户加权。",
      "LEFT JOIN 保留存在已完成订单但缺少订单行的客户，并将其消费计为 0。",
      "仅使用已完成订单，且净销售额严格扣除 discount_amount。"
    ],
    "sources": [
      "fact_orders",
      "fact_order_items",
      "dim_customers"
    ],
    "steps": [
      "按客户汇总全部已完成订单行净销售额。",
      "在客户汇总结果上计算全体客户平均累计消费。",
      "筛选累计消费高于平均值的客户。",
      "按 total_spend 降序、customer_id 升序排序。"
    ]
  },
  "prompt": "你是 Text-to-SQL 数据分析与数据开发生成器。先形成简洁、可审计的结构化查询方案，再生成完成问题所需的 SQL；不要输出隐藏推理。\n\n方言与安全规则：\nUse DuckDB SQL. Return exactly one read-only query. Do not access files, URLs, extensions, or schemas outside the supplied tables.\n\n数据库结构：\n{\"semantic_relationships\":[{\"cardinality\":\"many_to_one\",\"from_entity\":\"order\",\"sql_on\":\"fact_orders.customer_id = dim_customers.customer_id\",\"to_entity\":\"customer\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"order\",\"sql_on\":\"fact_orders.channel_id = dim_channels.channel_id\",\"to_entity\":\"channel\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"order_item\",\"sql_on\":\"fact_order_items.order_id = fact_orders.order_id\",\"to_entity\":\"order\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"order_item\",\"sql_on\":\"fact_order_items.product_id = dim_products.product_id\",\"to_entity\":\"product\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"payment\",\"sql_on\":\"fact_payments.order_id = fact_orders.order_id\",\"to_entity\":\"order\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"return\",\"sql_on\":\"fact_returns.order_id = fact_order_items.order_id AND fact_returns.line_no = fact_order_items.line_no\",\"to_entity\":\"order_item\"}],\"tables\":[{\"columns\":[{\"data_type\":\"BIGINT\",\"name\":\"channel_id\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"channel_name\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"channel_type\",\"nullable\":false}],\"foreign_keys\":[],\"name\":\"dim_channels\",\"primary_key\":[\"channel_id\"]},{\"columns\":[{\"data_type\":\"BIGINT\",\"name\":\"customer_id\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"customer_name\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"city\",\"nullable\":true},{\"data_type\":\"DATE\",\"name\":\"signup_date\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"segment\",\"nullable\":false}],\"foreign_keys\":[],\"name\":\"dim_customers\",\"primary_key\":[\"customer_id\"]},{\"columns\":[{\"data_type\":\"BIGINT\",\"name\":\"product_id\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"product_name\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"category\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"brand\",\"nullable\":false},{\"data_type\":\"DECIMAL(14,2)\",\"name\":\"list_price\",\"nullable\":false}],\"foreign_keys\":[],\"name\":\"dim_products\",\"primary_key\":[\"product_id\"]},{\"columns\":[{\"data_type\":\"BIGINT\",\"name\":\"order_id\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"line_no\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"product_id\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"quantity\",\"nullable\":false},{\"data_type\":\"DECIMAL(14,2)\",\"name\":\"unit_price\",\"nullable\":false},{\"data_type\":\"DECIMAL(14,2)\",\"name\":\"discount_amount\",\"nullable\":false}],\"foreign_keys\":[{\"columns\":[\"order_id\"],\"referenced_columns\":[\"order_id\"],\"referenced_table\":\"fact_orders\"},{\"columns\":[\"product_id\"],\"referenced_columns\":[\"product_id\"],\"referenced_table\":\"dim_products\"}],\"name\":\"fact_order_items\",\"primary_key\":[\"order_id\",\"line_no\"]},{\"columns\":[{\"data_type\":\"BIGINT\",\"name\":\"order_id\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"customer_id\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"channel_id\",\"nullable\":false},{\"data_type\":\"DATE\",\"name\":\"order_date\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"status\",\"nullable\":false},{\"data_type\":\"DECIMAL(14,2)\",\"name\":\"total_amount\",\"nullable\":false}],\"foreign_keys\":[{\"columns\":[\"customer_id\"],\"referenced_columns\":[\"customer_id\"],\"referenced_table\":\"dim_customers\"},{\"columns\":[\"channel_id\"],\"referenced_columns\":[\"channel_id\"],\"referenced_table\":\"dim_channels\"}],\"name\":\"fact_orders\",\"primary_key\":[\"order_id\"]},{\"columns\":[{\"data_type\":\"BIGINT\",\"name\":\"payment_id\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"order_id\",\"nullable\":false},{\"data_type\":\"TIMESTAMP\",\"name\":\"paid_at\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"payment_method\",\"nullable\":false},{\"data_type\":\"DECIMAL(14,2)\",\"name\":\"amount\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"status\",\"nullable\":false}],\"foreign_keys\":[{\"columns\":[\"order_id\"],\"referenced_columns\":[\"order_id\"],\"referenced_table\":\"fact_orders\"}],\"name\":\"fact_payments\",\"primary_key\":[\"payment_id\"]},{\"columns\":[{\"data_type\":\"BIGINT\",\"name\":\"return_id\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"order_id\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"line_no\",\"nullable\":false},{\"data_type\":\"TIMESTAMP\",\"name\":\"returned_at\",\"nullable\":false},{\"data_type\":\"BIGINT\",\"name\":\"return_qty\",\"nullable\":false},{\"data_type\":\"DECIMAL(14,2)\",\"name\":\"refund_amount\",\"nullable\":false},{\"data_type\":\"VARCHAR\",\"name\":\"reason\",\"nullable\":true}],\"foreign_keys\":[{\"columns\":[\"order_id\",\"line_no\"],\"referenced_columns\":[\"order_id\",\"line_no\"],\"referenced_table\":\"fact_order_items\"}],\"name\":\"fact_returns\",\"primary_key\":[\"return_id\"]}]}\n\n语义层与业务口径：\n{\"business_rules\":[\"完成订单仅指 fact_orders.status = 'completed'。\",\"净销售额为 quantity * unit_price - discount_amount。\",\"paid、refunded、failed 金额只按 fact_payments.status 分类。\",\"退货率为完成订单的 returned_qty / sold_qty，售出数量仅含完成订单。\",\"月份按 UTC Gregorian calendar 计算。\",\"订单头 total_amount 应等于订单行净额汇总，差异视为数据质量异常。\"],\"dimensions\":[{\"data_type\":\"VARCHAR\",\"description\":\"客户分群\",\"expression\":\"dim_customers.segment\",\"name\":\"customer_segment\"},{\"data_type\":\"VARCHAR\",\"description\":\"商品品类\",\"expression\":\"dim_products.category\",\"name\":\"product_category\"},{\"data_type\":\"VARCHAR\",\"description\":\"渠道类型\",\"expression\":\"dim_channels.channel_type\",\"name\":\"channel_type\"},{\"data_type\":\"VARCHAR\",\"description\":\"UTC Gregorian 月份\",\"expression\":\"strftime(fact_orders.order_date, '%Y-%m')\",\"name\":\"order_month\"}],\"entities\":[{\"description\":\"客户主数据\",\"grain\":\"每行一个客户\",\"name\":\"customer\",\"primary_key\":[\"customer_id\"],\"table\":\"dim_customers\"},{\"description\":\"商品主数据\",\"grain\":\"每行一个商品\",\"name\":\"product\",\"primary_key\":[\"product_id\"],\"table\":\"dim_products\"},{\"description\":\"渠道主数据\",\"grain\":\"每行一个渠道\",\"name\":\"channel\",\"primary_key\":[\"channel_id\"],\"table\":\"dim_channels\"},{\"description\":\"订单头\",\"grain\":\"每行一个订单\",\"name\":\"order\",\"primary_key\":[\"order_id\"],\"table\":\"fact_orders\"},{\"description\":\"订单行\",\"grain\":\"每行一个订单商品行\",\"name\":\"order_item\",\"primary_key\":[\"order_id\",\"line_no\"],\"table\":\"fact_order_items\"},{\"description\":\"支付尝试\",\"grain\":\"每行一笔支付\",\"name\":\"payment\",\"primary_key\":[\"payment_id\"],\"table\":\"fact_payments\"},{\"description\":\"退货记录\",\"grain\":\"每行一条订单行退货\",\"name\":\"return\",\"primary_key\":[\"return_id\"],\"table\":\"fact_returns\"}],\"metrics\":[{\"description\":\"已完成订单数\",\"expression\":\"COUNT(DISTINCT CASE WHEN fact_orders.status = 'completed' THEN fact_orders.order_id END)\",\"filters\":[\"fact_orders.status = 'completed'\"],\"grain\":\"聚合\",\"name\":\"completed_order_count\"},{\"description\":\"完成订单商品行净销售额\",\"expression\":\"SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount)\",\"filters\":[\"fact_orders.status = 'completed'\"],\"grain\":\"聚合\",\"name\":\"net_revenue\"},{\"description\":\"成功支付金额\",\"expression\":\"SUM(CASE WHEN fact_payments.status = 'paid' THEN fact_payments.amount ELSE 0 END)\",\"filters\":[],\"grain\":\"聚合\",\"name\":\"paid_amount\"},{\"description\":\"退款状态支付金额\",\"expression\":\"SUM(CASE WHEN fact_payments.status = 'refunded' THEN fact_payments.amount ELSE 0 END)\",\"filters\":[],\"grain\":\"聚合\",\"name\":\"refunded_amount\"},{\"description\":\"失败支付金额\",\"expression\":\"SUM(CASE WHEN fact_payments.status = 'failed' THEN fact_payments.amount ELSE 0 END)\",\"filters\":[],\"grain\":\"聚合\",\"name\":\"failed_amount\"},{\"description\":\"完成订单退货数量除以售出数量\",\"expression\":\"SUM(fact_returns.return_qty) / SUM(fact_order_items.quantity)\",\"filters\":[\"fact_orders.status = 'completed'\"],\"grain\":\"聚合\",\"name\":\"return_rate\"}],\"relationships\":[{\"cardinality\":\"many_to_one\",\"from_entity\":\"order\",\"sql_on\":\"fact_orders.customer_id = dim_customers.customer_id\",\"to_entity\":\"customer\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"order\",\"sql_on\":\"fact_orders.channel_id = dim_channels.channel_id\",\"to_entity\":\"channel\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"order_item\",\"sql_on\":\"fact_order_items.order_id = fact_orders.order_id\",\"to_entity\":\"order\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"order_item\",\"sql_on\":\"fact_order_items.product_id = dim_products.product_id\",\"to_entity\":\"product\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"payment\",\"sql_on\":\"fact_payments.order_id = fact_orders.order_id\",\"to_entity\":\"order\"},{\"cardinality\":\"many_to_one\",\"from_entity\":\"return\",\"sql_on\":\"fact_returns.order_id = fact_order_items.order_id AND fact_returns.line_no = fact_order_items.line_no\",\"to_entity\":\"order_item\"}]}\n\n问题：\n找出累计已完成消费高于有已完成订单客户平均累计消费的客户，输出 customer_id、customer_name、total_spend，并按 total_spend 降序、customer_id 升序；必须至少两层 derived table 或子查询。\n\n规划要求：\nplan 只记录输出粒度、数据源、连接、过滤、指标、执行步骤和风险检查；每项保持简短。\nSQL 必须严格实现该 plan，并遵循语义层业务口径。\nsummary 只概括最终做法，assumptions 只列无法由上下文确定的必要假设。\n输出必须严格匹配此 JSON Schema：\n{\"$defs\":{\"QueryPlan\":{\"additionalProperties\":false,\"properties\":{\"filters\":{\"items\":{\"type\":\"string\"},\"title\":\"Filters\",\"type\":\"array\"},\"grain\":{\"title\":\"Grain\",\"type\":\"string\"},\"joins\":{\"items\":{\"type\":\"string\"},\"title\":\"Joins\",\"type\":\"array\"},\"metrics\":{\"items\":{\"type\":\"string\"},\"title\":\"Metrics\",\"type\":\"array\"},\"risks\":{\"items\":{\"type\":\"string\"},\"title\":\"Risks\",\"type\":\"array\"},\"sources\":{\"items\":{\"type\":\"string\"},\"title\":\"Sources\",\"type\":\"array\"},\"steps\":{\"items\":{\"type\":\"string\"},\"title\":\"Steps\",\"type\":\"array\"}},\"required\":[\"grain\",\"sources\",\"joins\",\"filters\",\"metrics\",\"steps\",\"risks\"],\"title\":\"QueryPlan\",\"type\":\"object\"}},\"additionalProperties\":false,\"properties\":{\"assumptions\":{\"items\":{\"type\":\"string\"},\"title\":\"Assumptions\",\"type\":\"array\"},\"plan\":{\"$ref\":\"#/$defs/QueryPlan\"},\"sql\":{\"title\":\"Sql\",\"type\":\"string\"},\"summary\":{\"title\":\"Summary\",\"type\":\"string\"}},\"required\":[\"plan\",\"sql\",\"summary\",\"assumptions\"],\"title\":\"GenerationOutput\",\"type\":\"object\"}",
  "provider_request_id": null,
  "question": "找出累计已完成消费高于有已完成订单客户平均累计消费的客户，输出 customer_id、customer_name、total_spend，并按 total_spend 降序、customer_id 升序；必须至少两层 derived table 或子查询。",
  "radar_dimension": "复杂查询",
  "raw_output": "{\"plan\":{\"grain\":\"每行一个高于基准的客户。\",\"sources\":[\"fact_orders\",\"fact_order_items\",\"dim_customers\"],\"joins\":[\"fact_orders.customer_id = dim_customers.customer_id\",\"fact_order_items.order_id = fact_orders.order_id（LEFT JOIN）\"],\"filters\":[\"fact_orders.status = 'completed'\",\"total_spend > 有已完成订单客户的平均 total_spend\"],\"metrics\":[\"total_spend = SUM(quantity * unit_price - discount_amount)\",\"平均累计消费 = AVG(customer-level total_spend)\"],\"steps\":[\"按客户汇总全部已完成订单行净销售额。\",\"在客户汇总结果上计算全体客户平均累计消费。\",\"筛选累计消费高于平均值的客户。\",\"按 total_spend 降序、customer_id 升序排序。\"],\"risks\":[\"先按客户聚合再计算平均，避免订单行数造成客户加权。\",\"LEFT JOIN 保留存在已完成订单但缺少订单行的客户，并将其消费计为 0。\",\"仅使用已完成订单，且净销售额严格扣除 discount_amount。\"]},\"sql\":\"SELECT\\n    customer_id,\\n    customer_name,\\n    total_spend\\nFROM (\\n    SELECT\\n        customer_spend.customer_id,\\n        customer_spend.customer_name,\\n        customer_spend.total_spend,\\n        AVG(customer_spend.total_spend) OVER () AS avg_total_spend\\n    FROM (\\n        SELECT\\n            c.customer_id,\\n            c.customer_name,\\n            SUM(\\n                COALESCE(\\n                    oi.quantity * oi.unit_price - oi.discount_amount,\\n                    CAST(0 AS DECIMAL(14, 2))\\n                )\\n            ) AS total_spend\\n        FROM fact_orders AS o\\n        INNER JOIN dim_customers AS c\\n            ON o.customer_id = c.customer_id\\n        LEFT JOIN fact_order_items AS oi\\n            ON o.order_id = oi.order_id\\n        WHERE o.status = 'completed'\\n        GROUP BY\\n            c.customer_id,\\n            c.customer_name\\n    ) AS customer_spend\\n) AS customer_spend_with_average\\nWHERE total_spend > avg_total_spend\\nORDER BY\\n    total_spend DESC,\\n    customer_id ASC\",\"summary\":\"先汇总每位有已完成订单客户的订单行净销售额，再通过外层派生表计算客户平均累计消费，筛出高于平均值者并按指定顺序输出。\",\"assumptions\":[]}",
  "reference_sql": "SELECT spend.customer_id, c.customer_name, spend.total_spend FROM (SELECT customer_id, SUM(total_amount) AS total_spend FROM fact_orders WHERE status = 'completed' GROUP BY customer_id) spend JOIN dim_customers c ON c.customer_id = spend.customer_id WHERE spend.total_spend > (SELECT AVG(avg_source.total_spend) FROM (SELECT customer_id, SUM(total_amount) AS total_spend FROM fact_orders WHERE status = 'completed' GROUP BY customer_id) avg_source) ORDER BY spend.total_spend DESC, spend.customer_id ASC",
  "requested_model_id": "gpt-session-bridge",
  "required_ast": [
    {
      "id": "depth-3",
      "kind": "query_depth",
      "min": 3
    }
  ],
  "resolved_model_id": "gpt-session-bridge",
  "result_preview": {
    "columns": [
      {
        "name": "customer_id",
        "type": "BIGINT"
      },
      {
        "name": "customer_name",
        "type": "VARCHAR"
      },
      {
        "name": "total_spend",
        "type": "DECIMAL(38,2)"
      }
    ],
    "extra": [
      [
        "1.00",
        "客户-001",
        "9411.75"
      ]
    ],
    "missing": [
      [
        "1.00",
        "客户-001",
        "9421.75"
      ]
    ],
    "row_count": 53,
    "rows": [
      [
        17,
        "客户-017",
        "12860.50"
      ],
      [
        107,
        "客户-107",
        "12370.50"
      ],
      [
        104,
        "客户-104",
        "11772.25"
      ],
      [
        13,
        "客户-013",
        "11433.25"
      ],
      [
        21,
        "客户-021",
        "10899.00"
      ],
      [
        44,
        "客户-044",
        "10014.50"
      ],
      [
        80,
        "客户-080",
        "9556.25"
      ],
      [
        1,
        "客户-001",
        "9411.75"
      ],
      [
        67,
        "客户-067",
        "9322.00"
      ],
      [
        65,
        "客户-065",
        "9175.75"
      ],
      [
        85,
        "客户-085",
        "9157.75"
      ],
      [
        12,
        "客户-012",
        "8901.75"
      ],
      [
        2,
        "客户-002",
        "8572.00"
      ],
      [
        43,
        "客户-043",
        "8442.00"
      ],
      [
        95,
        "客户-095",
        "8306.00"
      ],
      [
        94,
        "客户-094",
        "8156.50"
      ],
      [
        46,
        "客户-046",
        "8136.50"
      ],
      [
        29,
        "客户-029",
        "8083.75"
      ],
      [
        109,
        "客户-109",
        "8003.75"
      ],
      [
        10,
        "客户-010",
        "7933.75"
      ],
      [
        31,
        "客户-031",
        "7833.75"
      ],
      [
        69,
        "客户-069",
        "7636.00"
      ],
      [
        59,
        "客户-059",
        "7491.50"
      ],
      [
        53,
        "客户-053",
        "7487.00"
      ],
      [
        20,
        "客户-020",
        "7482.75"
      ],
      [
        70,
        "客户-070",
        "7278.00"
      ],
      [
        38,
        "客户-038",
        "7042.00"
      ],
      [
        15,
        "客户-015",
        "6957.50"
      ],
      [
        56,
        "客户-056",
        "6878.50"
      ],
      [
        82,
        "客户-082",
        "6805.25"
      ],
      [
        74,
        "客户-074",
        "6759.50"
      ],
      [
        48,
        "客户-048",
        "6726.00"
      ],
      [
        36,
        "客户-036",
        "6578.00"
      ],
      [
        14,
        "客户-014",
        "6505.00"
      ],
      [
        61,
        "客户-061",
        "6371.75"
      ],
      [
        50,
        "客户-050",
        "6294.50"
      ],
      [
        78,
        "客户-078",
        "6170.25"
      ],
      [
        89,
        "客户-089",
        "6129.25"
      ],
      [
        5,
        "客户-005",
        "6070.25"
      ],
      [
        34,
        "客户-034",
        "5893.00"
      ],
      [
        32,
        "客户-032",
        "5828.75"
      ],
      [
        93,
        "客户-093",
        "5799.25"
      ],
      [
        71,
        "客户-071",
        "5791.25"
      ],
      [
        24,
        "客户-024",
        "5717.00"
      ],
      [
        106,
        "客户-106",
        "5662.25"
      ],
      [
        105,
        "客户-105",
        "5632.25"
      ],
      [
        60,
        "客户-060",
        "5597.50"
      ],
      [
        39,
        "客户-039",
        "5563.00"
      ],
      [
        92,
        "客户-092",
        "5520.50"
      ],
      [
        45,
        "客户-045",
        "5449.75"
      ],
      [
        41,
        "客户-041",
        "5355.50"
      ],
      [
        49,
        "客户-049",
        "5309.00"
      ],
      [
        52,
        "客户-052",
        "5189.00"
      ]
    ]
  },
  "run_id": 16,
  "score": {
    "ast_rules": [
      {
        "details": {
          "actual": 3,
          "required": 3
        },
        "id": "depth-3",
        "kind": "query_depth",
        "passed": true
      }
    ],
    "column_count": 5,
    "column_names": 5,
    "execution": 10,
    "ordering": 0,
    "protocol": 5,
    "read_only_ast": 5,
    "row_f1": 44.15094339622642,
    "sql_capability": 15,
    "total": 89.15
  },
  "stable_key": "above_average_customer_spend",
  "started_at": "2026-08-29T22:07:34.786856",
  "status": "completed",
  "suite_content_hash": "5b5d98876ea35114f18ce6dfa48cc9800d88b6baba80d311b2f52552a38b31af",
  "title": "高于平均累计消费客户",
  "token_usage": {
    "completion_tokens": 443,
    "prompt_tokens": 2378,
    "total_tokens": 2821
  },
  "visible_summary": "先汇总每位有已完成订单客户的订单行净销售额，再通过外层派生表计算客户平均累计消费，筛出高于平均值者并按指定顺序输出。"
}