拾拾穗数据工作室SQL 评测报告与证据

案例记录 case-run-00023

运行 #11/category_return_rate

品类退货率

先分别聚合已完成订单的售出数量和退货数量，再计算品类退货率；输出 category、sold_qty、returned_qty、return_rate，比率四舍五入 4 位，按 return_rate 降序、category 升序；必须至少两个 CTE 且不得明细直接 JOIN 造成 fan-out。

运行信息

以下字段来自本次运行的冻结记录。

run#11
case run#23
attempt1
statuscompleted
token total19,587
estimated cost不可估算
model generation未记录
SQL execution236ms
adaptercodex_cli
response modetext
requested modelgpt-5.6-sol
resolved modelgpt-5.6-sol
provider request id未提供
started2026年8月29日 16:29
finished2026年8月29日 16:29
suite hash0a4a18b4374f510f5eff18b06272c30c3375e1f082ae405adc8ead7dd9c81556

模型输入

显示本次实际保存的完整 Prompt。

你是 Text-to-SQL 生成器。只生成完成问题所需的 SQL 和简短可见摘要，不输出隐藏推理。

方言与安全规则：
Use DuckDB SQL. Return exactly one read-only query. Do not access files, URLs, extensions, or schemas outside the supplied tables.

数据库结构：
{"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"]}]}

语义层与业务口径：
{"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。"],"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"}]}

问题：
先分别聚合已完成订单的售出数量和退货数量，再计算品类退货率；输出 category、sold_qty、returned_qty、return_rate，比率四舍五入 4 位，按 return_rate 降序、category 升序；必须至少两个 CTE 且不得明细直接 JOIN 造成 fan-out。

输出必须严格匹配此 JSON Schema：
{"additionalProperties":false,"properties":{"assumptions":{"items":{"type":"string"},"title":"Assumptions","type":"array"},"sql":{"title":"Sql","type":"string"},"summary":{"title":"Summary","type":"string"}},"required":["sql","summary","assumptions"],"title":"GenerationOutput","type":"object"}

调用请求（1）

按事件序号排列；密钥和 Authorization 已移除。

历史证据边界：完整 Prompt 已保存，但这次 codex_cli 记录没有保存底层 HTTP 报文或 CLI 参数。本站不会把推测内容伪装成原始请求；未来运行会在 provider.requested 中记录脱敏后的完整调用信封。

请求 #1SEQ 124
{
  "isolation_policy_hash": "2c8225f927aec6ea94b3dfaf5e08e2ff402f5a44781dd908500854a6498593a0",
  "status": "running"
}

模型输出

分别显示原始响应、解析结果和 SQL。

原始响应 raw_output
{"sql":"WITH sold AS (\n    SELECT\n        p.category,\n        SUM(oi.quantity) AS sold_qty\n    FROM fact_order_items AS oi\n    JOIN fact_orders AS o\n      ON o.order_id = oi.order_id\n    JOIN dim_products AS p\n      ON p.product_id = oi.product_id\n    WHERE o.status = 'completed'\n    GROUP BY p.category\n),\nreturned AS (\n    SELECT\n        p.category,\n        SUM(r.return_qty) AS returned_qty\n    FROM fact_returns AS r\n    JOIN fact_order_items AS oi\n      ON oi.order_id = r.order_id\n     AND oi.line_no = r.line_no\n    JOIN fact_orders AS o\n      ON o.order_id = oi.order_id\n    JOIN dim_products AS p\n      ON p.product_id = oi.product_id\n    WHERE o.status = 'completed'\n    GROUP BY p.category\n),\ncategory_rates AS (\n    SELECT\n        s.category,\n        s.sold_qty,\n        COALESCE(r.returned_qty, 0) AS returned_qty,\n        ROUND(COALESCE(r.returned_qty, 0)::DECIMAL / NULLIF(s.sold_qty, 0), 4) AS return_rate\n    FROM sold AS s\n    LEFT JOIN returned AS r\n      ON r.category = s.category\n)\nSELECT\n    category,\n    sold_qty,\n    returned_qty,\n    return_rate\nFROM category_rates\nORDER BY return_rate DESC, category ASC","summary":"分别按品类聚合完成订单的售出数量与退货数量，再计算四舍五入至 4 位的退货率，并按退货率降序、品类升序排列。","assumptions":["无退货记录的品类 returned_qty 及 return_rate 均按 0 处理。","退货率按退货数量除以完成订单售出数量计算。"]}
结构化查询方案 plan

没有可解析方案。

模型生成 SQL
WITH sold AS (
    SELECT
        p.category,
        SUM(oi.quantity) AS sold_qty
    FROM fact_order_items AS oi
    JOIN fact_orders AS o
      ON o.order_id = oi.order_id
    JOIN dim_products AS p
      ON p.product_id = oi.product_id
    WHERE o.status = 'completed'
    GROUP BY p.category
),
returned AS (
    SELECT
        p.category,
        SUM(r.return_qty) AS returned_qty
    FROM fact_returns AS r
    JOIN fact_order_items AS oi
      ON oi.order_id = r.order_id
     AND oi.line_no = r.line_no
    JOIN fact_orders AS o
      ON o.order_id = oi.order_id
    JOIN dim_products AS p
      ON p.product_id = oi.product_id
    WHERE o.status = 'completed'
    GROUP BY p.category
),
category_rates AS (
    SELECT
        s.category,
        s.sold_qty,
        COALESCE(r.returned_qty, 0) AS returned_qty,
        ROUND(COALESCE(r.returned_qty, 0)::DECIMAL / NULLIF(s.sold_qty, 0), 4) AS return_rate
    FROM sold AS s
    LEFT JOIN returned AS r
      ON r.category = s.category
)
SELECT
    category,
    sold_qty,
    returned_qty,
    return_rate
FROM category_rates
ORDER BY return_rate DESC, category ASC
规范化 SQL
WITH sold AS (
  SELECT
    p.category,
    SUM(oi.quantity) AS sold_qty
  FROM fact_order_items AS oi
  JOIN fact_orders AS o
    ON o.order_id = oi.order_id
  JOIN dim_products AS p
    ON p.product_id = oi.product_id
  WHERE
    o.status = 'completed'
  GROUP BY
    p.category
), returned AS (
  SELECT
    p.category,
    SUM(r.return_qty) AS returned_qty
  FROM fact_returns AS r
  JOIN fact_order_items AS oi
    ON oi.order_id = r.order_id AND oi.line_no = r.line_no
  JOIN fact_orders AS o
    ON o.order_id = oi.order_id
  JOIN dim_products AS p
    ON p.product_id = oi.product_id
  WHERE
    o.status = 'completed'
  GROUP BY
    p.category
), category_rates AS (
  SELECT
    s.category,
    s.sold_qty,
    COALESCE(r.returned_qty, 0) AS returned_qty,
    ROUND(CAST(COALESCE(r.returned_qty, 0) AS DECIMAL(18, 3)) / NULLIF(s.sold_qty, 0), 4) AS return_rate
  FROM sold AS s
  LEFT JOIN returned AS r
    ON r.category = s.category
)
SELECT
  category,
  sold_qty,
  returned_qty,
  return_rate
FROM category_rates
ORDER BY
  return_rate DESC,
  category ASC
Token 与耗时
{
  "token_usage": {
    "cache_write_input_tokens": 0,
    "cached_input_tokens": 9984,
    "input_tokens": 19164,
    "output_tokens": 423,
    "reasoning_output_tokens": 0
  },
  "generation_ms": null,
  "execution_ms": 235.67491699941456
}

评分结果

包含评分明细、参考 SQL、结构要求、比较规则和结果差异。

评分明细 score
{
  "ast_rules": [
    {
      "details": {
        "actual": 3,
        "required": 2
      },
      "id": "cte-2",
      "kind": "cte_count",
      "passed": true
    },
    {
      "details": {},
      "id": "preaggregate",
      "kind": "separate_measure_preaggregation",
      "passed": false
    }
  ],
  "column_count": 5,
  "column_names": 5,
  "execution": 10,
  "ordering": 10,
  "protocol": 5,
  "read_only_ast": 5,
  "row_f1": 45,
  "sql_capability": 7.5,
  "total": 92.5
}
参考 SQL
WITH sold AS (SELECT p.category, SUM(i.quantity) AS sold_qty FROM fact_order_items i JOIN fact_orders o ON o.order_id = i.order_id JOIN dim_products p ON p.product_id = i.product_id WHERE o.status = 'completed' GROUP BY p.category), returned AS (SELECT p.category, SUM(r.return_qty) AS returned_qty FROM fact_returns r JOIN fact_order_items i ON i.order_id = r.order_id AND i.line_no = r.line_no JOIN fact_orders o ON o.order_id = i.order_id JOIN dim_products p ON p.product_id = i.product_id WHERE o.status = 'completed' GROUP BY p.category) SELECT s.category, s.sold_qty, COALESCE(r.returned_qty, 0) AS returned_qty, ROUND(COALESCE(r.returned_qty, 0)::DECIMAL / NULLIF(s.sold_qty, 0), 4) AS return_rate FROM sold s LEFT JOIN returned r ON r.category = s.category ORDER BY return_rate DESC, s.category ASC
AST 结构要求
[
  {
    "id": "cte-2",
    "kind": "cte_count",
    "min": 2
  },
  {
    "group_keys": [
      "category"
    ],
    "id": "preaggregate",
    "kind": "separate_measure_preaggregation",
    "left_cte": "sold",
    "left_measure_column": "quantity",
    "left_measure_table": "fact_order_items",
    "right_cte": "returned",
    "right_measure_column": "return_qty",
    "right_measure_table": "fact_returns"
  }
]
结果比较规则
{
  "abs_tolerance": "0.00005",
  "decimal_scale": 4,
  "duplicate_policy": "multiset",
  "max_rows": 10000,
  "rel_tolerance": "0",
  "row_order_significant": true
}
期望结果预览
{
  "columns": [
    {
      "name": "category",
      "type": "VARCHAR"
    },
    {
      "name": "sold_qty",
      "type": "HUGEINT"
    },
    {
      "name": "returned_qty",
      "type": "HUGEINT"
    },
    {
      "name": "return_rate",
      "type": "DOUBLE"
    }
  ],
  "digest": "99e800276b57086673568cace5171e4c98477f08dee221fcce7c2b8330e6e7bb",
  "row_count": 6,
  "rows": [
    [
      "品类-5",
      531,
      58,
      "0.1092"
    ],
    [
      "品类-3",
      624,
      52,
      "0.0833"
    ],
    [
      "品类-4",
      592,
      49,
      "0.0828"
    ],
    [
      "品类-6",
      667,
      50,
      "0.075"
    ],
    [
      "品类-2",
      649,
      25,
      "0.0385"
    ],
    [
      "品类-1",
      592,
      17,
      "0.0287"
    ]
  ]
}
实际结果预览
{
  "columns": [
    {
      "name": "category",
      "type": "VARCHAR"
    },
    {
      "name": "sold_qty",
      "type": "HUGEINT"
    },
    {
      "name": "returned_qty",
      "type": "HUGEINT"
    },
    {
      "name": "return_rate",
      "type": "DOUBLE"
    }
  ],
  "extra": [],
  "missing": [],
  "row_count": 6,
  "rows": [
    [
      "品类-5",
      531,
      58,
      0.1092
    ],
    [
      "品类-3",
      624,
      52,
      0.0833
    ],
    [
      "品类-4",
      592,
      49,
      0.0828
    ],
    [
      "品类-6",
      667,
      50,
      0.075
    ],
    [
      "品类-2",
      649,
      25,
      0.0385
    ],
    [
      "品类-1",
      592,
      17,
      0.0287
    ]
  ]
}
摘要、错误与结果哈希
{
  "visible_summary": "分别按品类聚合完成订单的售出数量与退货数量，再计算四舍五入至 4 位的退货率，并按退货率降序、品类升序排列。",
  "error_code": null,
  "error_message": null,
  "expected_digest": "99e800276b57086673568cace5171e4c98477f08dee221fcce7c2b8330e6e7bb",
  "actual_digest": "2e32ecaea391c910b9fa383009854971ae182bbc3b881ebf8e20c038fd87eca3",
  "assumptions": null
}

事件记录（7）

按全局事件序号排列，保留时间、级别、类型、消息和 payload。

SEQ 122case.startedinfo
{
  "status": "generating"
}
SEQ 123prompt.builtinfo
{
  "status": "completed"
}
SEQ 124provider.requestedinfo
{
  "isolation_policy_hash": "2c8225f927aec6ea94b3dfaf5e08e2ff402f5a44781dd908500854a6498593a0",
  "status": "running"
}
SEQ 125provider.deltainfo
{
  "text": "{\"sql\":\"WITH sold AS (\\n    SELECT\\n        p.category,\\n        SUM(oi.quantity) AS sold_qty\\n    FROM fact_order_items AS oi\\n    JOIN fact_orders AS o\\n      ON o.order_id = oi.order_id\\n    JOIN dim_products AS p\\n      ON p.product_id = oi.product_id\\n    WHERE o.status = 'completed'\\n    GROUP BY p.category\\n),\\nreturned AS (\\n    SELECT\\n        p.category,\\n        SUM(r.return_qty) AS returned_qty\\n    FROM fact_returns AS r\\n    JOIN fact_order_items AS oi\\n      ON oi.order_id = r.order_id\\n     AND oi.line_no = r.line_no\\n    JOIN fact_orders AS o\\n      ON o.order_id = oi.order_id\\n    JOIN dim_products AS p\\n      ON p.product_id = oi.product_id\\n    WHERE o.status = 'completed'\\n    GROUP BY p.category\\n),\\ncategory_rates AS (\\n    SELECT\\n        s.category,\\n        s.sold_qty,\\n        COALESCE(r.returned_qty, 0) AS returned_qty,\\n        ROUND(COALESCE(r.returned_qty, 0)::DECIMAL / NULLIF(s.sold_qty, 0), 4) AS return_rate\\n    FROM sold AS s\\n    LEFT JOIN returned AS r\\n      ON r.category = s.category\\n)\\nSELECT\\n    category,\\n    sold_qty,\\n    returned_qty,\\n    return_rate\\nFROM category_rates\\nORDER BY return_rate DESC, category ASC\",\"summary\":\"分别按品类聚合完成订单的售出数量与退货数量，再计算四舍五入至 4 位的退货率，并按退货率降序、品类升序排列。\",\"assumptions\":[\"无退货记录的品类 returned_qty 及 return_rate 均按 0 处理。\",\"退货率按退货数量除以完成订单售出数量计算。\"]}"
}
SEQ 126provider.completedinfo
{
  "elapsed_ms": 22480.3092919974,
  "status": "completed",
  "token_usage": {
    "cache_write_input_tokens": 0,
    "cached_input_tokens": 9984,
    "input_tokens": 19164,
    "output_tokens": 423,
    "reasoning_output_tokens": 0
  }
}
SEQ 127sql.parsedinfo
{
  "status": "completed"
}
SEQ 128score.completedinfo
{
  "score": 92.5,
  "status": "completed"
}

原始案例数据

完整 JSON 字段，不经过页面裁剪。

展开全部原始字段
{
  "actual_digest": "2e32ecaea391c910b9fa383009854971ae182bbc3b881ebf8e20c038fd87eca3",
  "assumptions": null,
  "attempt": 1,
  "category": "multi_stage_cte",
  "comparison": {
    "abs_tolerance": "0.00005",
    "decimal_scale": 4,
    "duplicate_policy": "multiset",
    "max_rows": 10000,
    "rel_tolerance": "0",
    "row_order_significant": true
  },
  "difficulty": "hard",
  "error_code": null,
  "error_message": null,
  "execution_ms": 235.67491699941456,
  "expected_digest": "99e800276b57086673568cace5171e4c98477f08dee221fcce7c2b8330e6e7bb",
  "expected_result_preview": {
    "columns": [
      {
        "name": "category",
        "type": "VARCHAR"
      },
      {
        "name": "sold_qty",
        "type": "HUGEINT"
      },
      {
        "name": "returned_qty",
        "type": "HUGEINT"
      },
      {
        "name": "return_rate",
        "type": "DOUBLE"
      }
    ],
    "digest": "99e800276b57086673568cace5171e4c98477f08dee221fcce7c2b8330e6e7bb",
    "row_count": 6,
    "rows": [
      [
        "品类-5",
        531,
        58,
        "0.1092"
      ],
      [
        "品类-3",
        624,
        52,
        "0.0833"
      ],
      [
        "品类-4",
        592,
        49,
        "0.0828"
      ],
      [
        "品类-6",
        667,
        50,
        "0.075"
      ],
      [
        "品类-2",
        649,
        25,
        "0.0385"
      ],
      [
        "品类-1",
        592,
        17,
        "0.0287"
      ]
    ]
  },
  "finished_at": "2026-08-29T16:29:25.795705",
  "formatted_sql": "WITH sold AS (\n  SELECT\n    p.category,\n    SUM(oi.quantity) AS sold_qty\n  FROM fact_order_items AS oi\n  JOIN fact_orders AS o\n    ON o.order_id = oi.order_id\n  JOIN dim_products AS p\n    ON p.product_id = oi.product_id\n  WHERE\n    o.status = 'completed'\n  GROUP BY\n    p.category\n), returned AS (\n  SELECT\n    p.category,\n    SUM(r.return_qty) AS returned_qty\n  FROM fact_returns AS r\n  JOIN fact_order_items AS oi\n    ON oi.order_id = r.order_id AND oi.line_no = r.line_no\n  JOIN fact_orders AS o\n    ON o.order_id = oi.order_id\n  JOIN dim_products AS p\n    ON p.product_id = oi.product_id\n  WHERE\n    o.status = 'completed'\n  GROUP BY\n    p.category\n), category_rates AS (\n  SELECT\n    s.category,\n    s.sold_qty,\n    COALESCE(r.returned_qty, 0) AS returned_qty,\n    ROUND(CAST(COALESCE(r.returned_qty, 0) AS DECIMAL(18, 3)) / NULLIF(s.sold_qty, 0), 4) AS return_rate\n  FROM sold AS s\n  LEFT JOIN returned AS r\n    ON r.category = s.category\n)\nSELECT\n  category,\n  sold_qty,\n  returned_qty,\n  return_rate\nFROM category_rates\nORDER BY\n  return_rate DESC,\n  category ASC",
  "generated_sql": "WITH sold AS (\n    SELECT\n        p.category,\n        SUM(oi.quantity) AS sold_qty\n    FROM fact_order_items AS oi\n    JOIN fact_orders AS o\n      ON o.order_id = oi.order_id\n    JOIN dim_products AS p\n      ON p.product_id = oi.product_id\n    WHERE o.status = 'completed'\n    GROUP BY p.category\n),\nreturned AS (\n    SELECT\n        p.category,\n        SUM(r.return_qty) AS returned_qty\n    FROM fact_returns AS r\n    JOIN fact_order_items AS oi\n      ON oi.order_id = r.order_id\n     AND oi.line_no = r.line_no\n    JOIN fact_orders AS o\n      ON o.order_id = oi.order_id\n    JOIN dim_products AS p\n      ON p.product_id = oi.product_id\n    WHERE o.status = 'completed'\n    GROUP BY p.category\n),\ncategory_rates AS (\n    SELECT\n        s.category,\n        s.sold_qty,\n        COALESCE(r.returned_qty, 0) AS returned_qty,\n        ROUND(COALESCE(r.returned_qty, 0)::DECIMAL / NULLIF(s.sold_qty, 0), 4) AS return_rate\n    FROM sold AS s\n    LEFT JOIN returned AS r\n      ON r.category = s.category\n)\nSELECT\n    category,\n    sold_qty,\n    returned_qty,\n    return_rate\nFROM category_rates\nORDER BY return_rate DESC, category ASC",
  "generation_ms": null,
  "id": 23,
  "model_name": "Sol 本机实测",
  "model_run_id": 11,
  "plan": null,
  "prompt": "你是 Text-to-SQL 生成器。只生成完成问题所需的 SQL 和简短可见摘要，不输出隐藏推理。\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。\"],\"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先分别聚合已完成订单的售出数量和退货数量，再计算品类退货率；输出 category、sold_qty、returned_qty、return_rate，比率四舍五入 4 位，按 return_rate 降序、category 升序；必须至少两个 CTE 且不得明细直接 JOIN 造成 fan-out。\n\n输出必须严格匹配此 JSON Schema：\n{\"additionalProperties\":false,\"properties\":{\"assumptions\":{\"items\":{\"type\":\"string\"},\"title\":\"Assumptions\",\"type\":\"array\"},\"sql\":{\"title\":\"Sql\",\"type\":\"string\"},\"summary\":{\"title\":\"Summary\",\"type\":\"string\"}},\"required\":[\"sql\",\"summary\",\"assumptions\"],\"title\":\"GenerationOutput\",\"type\":\"object\"}",
  "provider_request_id": null,
  "question": "先分别聚合已完成订单的售出数量和退货数量，再计算品类退货率；输出 category、sold_qty、returned_qty、return_rate，比率四舍五入 4 位，按 return_rate 降序、category 升序；必须至少两个 CTE 且不得明细直接 JOIN 造成 fan-out。",
  "radar_dimension": "复杂管道",
  "raw_output": "{\"sql\":\"WITH sold AS (\\n    SELECT\\n        p.category,\\n        SUM(oi.quantity) AS sold_qty\\n    FROM fact_order_items AS oi\\n    JOIN fact_orders AS o\\n      ON o.order_id = oi.order_id\\n    JOIN dim_products AS p\\n      ON p.product_id = oi.product_id\\n    WHERE o.status = 'completed'\\n    GROUP BY p.category\\n),\\nreturned AS (\\n    SELECT\\n        p.category,\\n        SUM(r.return_qty) AS returned_qty\\n    FROM fact_returns AS r\\n    JOIN fact_order_items AS oi\\n      ON oi.order_id = r.order_id\\n     AND oi.line_no = r.line_no\\n    JOIN fact_orders AS o\\n      ON o.order_id = oi.order_id\\n    JOIN dim_products AS p\\n      ON p.product_id = oi.product_id\\n    WHERE o.status = 'completed'\\n    GROUP BY p.category\\n),\\ncategory_rates AS (\\n    SELECT\\n        s.category,\\n        s.sold_qty,\\n        COALESCE(r.returned_qty, 0) AS returned_qty,\\n        ROUND(COALESCE(r.returned_qty, 0)::DECIMAL / NULLIF(s.sold_qty, 0), 4) AS return_rate\\n    FROM sold AS s\\n    LEFT JOIN returned AS r\\n      ON r.category = s.category\\n)\\nSELECT\\n    category,\\n    sold_qty,\\n    returned_qty,\\n    return_rate\\nFROM category_rates\\nORDER BY return_rate DESC, category ASC\",\"summary\":\"分别按品类聚合完成订单的售出数量与退货数量，再计算四舍五入至 4 位的退货率，并按退货率降序、品类升序排列。\",\"assumptions\":[\"无退货记录的品类 returned_qty 及 return_rate 均按 0 处理。\",\"退货率按退货数量除以完成订单售出数量计算。\"]}",
  "reference_sql": "WITH sold AS (SELECT p.category, SUM(i.quantity) AS sold_qty FROM fact_order_items i JOIN fact_orders o ON o.order_id = i.order_id JOIN dim_products p ON p.product_id = i.product_id WHERE o.status = 'completed' GROUP BY p.category), returned AS (SELECT p.category, SUM(r.return_qty) AS returned_qty FROM fact_returns r JOIN fact_order_items i ON i.order_id = r.order_id AND i.line_no = r.line_no JOIN fact_orders o ON o.order_id = i.order_id JOIN dim_products p ON p.product_id = i.product_id WHERE o.status = 'completed' GROUP BY p.category) SELECT s.category, s.sold_qty, COALESCE(r.returned_qty, 0) AS returned_qty, ROUND(COALESCE(r.returned_qty, 0)::DECIMAL / NULLIF(s.sold_qty, 0), 4) AS return_rate FROM sold s LEFT JOIN returned r ON r.category = s.category ORDER BY return_rate DESC, s.category ASC",
  "requested_model_id": "gpt-5.6-sol",
  "required_ast": [
    {
      "id": "cte-2",
      "kind": "cte_count",
      "min": 2
    },
    {
      "group_keys": [
        "category"
      ],
      "id": "preaggregate",
      "kind": "separate_measure_preaggregation",
      "left_cte": "sold",
      "left_measure_column": "quantity",
      "left_measure_table": "fact_order_items",
      "right_cte": "returned",
      "right_measure_column": "return_qty",
      "right_measure_table": "fact_returns"
    }
  ],
  "resolved_model_id": "gpt-5.6-sol",
  "result_preview": {
    "columns": [
      {
        "name": "category",
        "type": "VARCHAR"
      },
      {
        "name": "sold_qty",
        "type": "HUGEINT"
      },
      {
        "name": "returned_qty",
        "type": "HUGEINT"
      },
      {
        "name": "return_rate",
        "type": "DOUBLE"
      }
    ],
    "extra": [],
    "missing": [],
    "row_count": 6,
    "rows": [
      [
        "品类-5",
        531,
        58,
        0.1092
      ],
      [
        "品类-3",
        624,
        52,
        0.0833
      ],
      [
        "品类-4",
        592,
        49,
        0.0828
      ],
      [
        "品类-6",
        667,
        50,
        0.075
      ],
      [
        "品类-2",
        649,
        25,
        0.0385
      ],
      [
        "品类-1",
        592,
        17,
        0.0287
      ]
    ]
  },
  "run_id": 11,
  "score": {
    "ast_rules": [
      {
        "details": {
          "actual": 3,
          "required": 2
        },
        "id": "cte-2",
        "kind": "cte_count",
        "passed": true
      },
      {
        "details": {},
        "id": "preaggregate",
        "kind": "separate_measure_preaggregation",
        "passed": false
      }
    ],
    "column_count": 5,
    "column_names": 5,
    "execution": 10,
    "ordering": 10,
    "protocol": 5,
    "read_only_ast": 5,
    "row_f1": 45,
    "sql_capability": 7.5,
    "total": 92.5
  },
  "stable_key": "category_return_rate",
  "started_at": "2026-08-29T16:29:03.053645",
  "status": "completed",
  "suite_content_hash": "0a4a18b4374f510f5eff18b06272c30c3375e1f082ae405adc8ead7dd9c81556",
  "title": "品类退货率",
  "token_usage": {
    "cache_write_input_tokens": 0,
    "cached_input_tokens": 9984,
    "input_tokens": 19164,
    "output_tokens": 423,
    "reasoning_output_tokens": 0
  },
  "visible_summary": "分别按品类聚合完成订单的售出数量与退货数量，再计算四舍五入至 4 位的退货率，并按退货率降序、品类升序排列。"
}
原始案例 JSON ↗完整运行事件 JSONL ↗返回运行 #11