请求 #1
SEQ 103{
"isolation_policy_hash": "57a5d1075866fc1fb1342e15d6029eaf80e0425db98f62a0cef0a56f6bd5f1e0",
"status": "running"
}案例记录 case-run-00044
月收入环比增长
输出 2025 年每月收入及环比百分比 month、monthly_revenue、mom_pct，首月为 NULL，其余四舍五入 2 位，按 month 升序；必须使用 LAG。
运行信息
以下字段来自本次运行的冻结记录。
run
#12case run
#44attempt
1status
completedtoken total
19,652estimated cost
不可估算model generation
未记录SQL execution
204msadapter
codex_cliresponse mode
textrequested model
gpt-5.6-solresolved model
gpt-5.6-solprovider request id
未提供started
2026年8月29日 16:40finished
2026年8月29日 16:40suite hash
0a4a18b4374f510f5eff18b06272c30c3375e1f082ae405adc8ead7dd9c81556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：
输出 2025 年每月收入及环比百分比 month、monthly_revenue、mom_pct，首月为 NULL，其余四舍五入 2 位，按 month 升序；必须使用 LAG。
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。
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 monthly AS (\n SELECT\n strftime(fact_orders.order_date, '%Y-%m') AS month,\n SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount) AS monthly_revenue\n FROM fact_orders\n JOIN fact_order_items\n ON fact_order_items.order_id = fact_orders.order_id\n WHERE fact_orders.status = 'completed'\n AND fact_orders.order_date >= DATE '2025-01-01'\n AND fact_orders.order_date < DATE '2026-01-01'\n GROUP BY 1\n), with_previous AS (\n SELECT\n month,\n monthly_revenue,\n LAG(monthly_revenue) OVER (ORDER BY month) AS previous_revenue\n FROM monthly\n)\nSELECT\n month,\n monthly_revenue,\n ROUND((monthly_revenue - previous_revenue) * 100.0 / NULLIF(previous_revenue, 0), 2) AS mom_pct\nFROM with_previous\nORDER BY month ASC;","summary":"按月汇总 2025 年完成订单的净销售额，并使用 LAG 计算环比百分比；首个有收入月份及上月收入为 0 时环比为 NULL。","assumptions":["收入采用语义层定义的完成订单商品行净销售额。","仅输出 2025 年存在完成订单收入记录的月份，不补齐无记录月份。"]}结构化查询方案 plan
没有可解析方案。
模型生成 SQL
WITH monthly AS (
SELECT
strftime(fact_orders.order_date, '%Y-%m') AS month,
SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount) AS monthly_revenue
FROM fact_orders
JOIN fact_order_items
ON fact_order_items.order_id = fact_orders.order_id
WHERE fact_orders.status = 'completed'
AND fact_orders.order_date >= DATE '2025-01-01'
AND fact_orders.order_date < DATE '2026-01-01'
GROUP BY 1
), with_previous AS (
SELECT
month,
monthly_revenue,
LAG(monthly_revenue) OVER (ORDER BY month) AS previous_revenue
FROM monthly
)
SELECT
month,
monthly_revenue,
ROUND((monthly_revenue - previous_revenue) * 100.0 / NULLIF(previous_revenue, 0), 2) AS mom_pct
FROM with_previous
ORDER BY month ASC;规范化 SQL
WITH monthly AS (
SELECT
STRFTIME(fact_orders.order_date, '%Y-%m') AS month,
SUM(
fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount
) AS monthly_revenue
FROM fact_orders
JOIN fact_order_items
ON fact_order_items.order_id = fact_orders.order_id
WHERE
fact_orders.status = 'completed'
AND fact_orders.order_date >= CAST('2025-01-01' AS DATE)
AND fact_orders.order_date < CAST('2026-01-01' AS DATE)
GROUP BY
1
), with_previous AS (
SELECT
month,
monthly_revenue,
LAG(monthly_revenue) OVER (ORDER BY month) AS previous_revenue
FROM monthly
)
SELECT
month,
monthly_revenue,
ROUND((
monthly_revenue - previous_revenue
) * 100.0 / NULLIF(previous_revenue, 0), 2) AS mom_pct
FROM with_previous
ORDER BY
month ASCToken 与耗时
{
"token_usage": {
"cache_write_input_tokens": 0,
"cached_input_tokens": 0,
"input_tokens": 19138,
"output_tokens": 514,
"reasoning_output_tokens": 187
},
"generation_ms": null,
"execution_ms": 203.9230000009411
}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": 1,
"required": 1
},
"id": "mom-lag",
"kind": "window_function",
"passed": true
}
],
"column_count": 5,
"column_names": 5,
"execution": 10,
"ordering": 10,
"protocol": 5,
"read_only_ast": 5,
"row_f1": 45,
"sql_capability": 15,
"total": 100
}参考 SQL
WITH monthly AS (SELECT strftime(order_date, '%Y-%m') AS month, SUM(total_amount) AS monthly_revenue FROM fact_orders WHERE status = 'completed' AND order_date >= DATE '2025-01-01' AND order_date < DATE '2026-01-01' GROUP BY month), lagged AS (SELECT month, monthly_revenue, LAG(monthly_revenue) OVER (ORDER BY month ASC) AS previous_revenue FROM monthly) SELECT month, monthly_revenue, ROUND((monthly_revenue - previous_revenue) / NULLIF(previous_revenue, 0) * 100, 2) AS mom_pct FROM lagged ORDER BY month ASCAST 结构要求
[
{
"id": "mom-lag",
"kind": "window_function",
"min": 1,
"name": "LAG",
"order_by": [
{
"direction": "ASC",
"expression": "month"
}
],
"partition_columns": []
}
]结果比较规则
{
"abs_tolerance": "0.005",
"decimal_scale": 2,
"duplicate_policy": "multiset",
"max_rows": 10000,
"rel_tolerance": "0",
"row_order_significant": true
}期望结果预览
{
"columns": [
{
"name": "month",
"type": "VARCHAR"
},
{
"name": "monthly_revenue",
"type": "DECIMAL(38,2)"
},
{
"name": "mom_pct",
"type": "DOUBLE"
}
],
"digest": "fccf4e8346d7523eb82d754179d442eacaffefa2dc0c4dfd3de2bf71f5a99ec0",
"row_count": 12,
"rows": [
[
"2025-01",
"41876.75",
null
],
[
"2025-02",
"31673.25",
"-24.37"
],
[
"2025-03",
"34373.25",
"8.52"
],
[
"2025-04",
"25069.00",
"-27.07"
],
[
"2025-05",
"30064.50",
"19.93"
],
[
"2025-06",
"31993.75",
"6.42"
],
[
"2025-07",
"36093.75",
"12.82"
],
[
"2025-08",
"29252.75",
"-18.95"
],
[
"2025-09",
"26246.25",
"-10.28"
],
[
"2025-10",
"21555.25",
"-17.87"
],
[
"2025-11",
"34944.25",
"62.11"
],
[
"2025-12",
"20373.00",
"-41.7"
]
]
}实际结果预览
{
"columns": [
{
"name": "month",
"type": "VARCHAR"
},
{
"name": "monthly_revenue",
"type": "DECIMAL(38,2)"
},
{
"name": "mom_pct",
"type": "DOUBLE"
}
],
"extra": [],
"missing": [],
"row_count": 12,
"rows": [
[
"2025-01",
"41876.75",
null
],
[
"2025-02",
"31673.25",
-24.37
],
[
"2025-03",
"34373.25",
8.52
],
[
"2025-04",
"25069.00",
-27.07
],
[
"2025-05",
"30064.50",
19.93
],
[
"2025-06",
"31993.75",
6.42
],
[
"2025-07",
"36093.75",
12.82
],
[
"2025-08",
"29252.75",
-18.95
],
[
"2025-09",
"26246.25",
-10.28
],
[
"2025-10",
"21555.25",
-17.87
],
[
"2025-11",
"34944.25",
62.11
],
[
"2025-12",
"20373.00",
-41.7
]
]
}摘要、错误与结果哈希
{
"visible_summary": "按月汇总 2025 年完成订单的净销售额，并使用 LAG 计算环比百分比；首个有收入月份及上月收入为 0 时环比为 NULL。",
"error_code": null,
"error_message": null,
"expected_digest": "fccf4e8346d7523eb82d754179d442eacaffefa2dc0c4dfd3de2bf71f5a99ec0",
"actual_digest": "721ba5e54f56af1da456da0c6d8cbd3e3bc9e4ee240f91f7f0bcdd347940cae3",
"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 101case.startedinfo{
"status": "generating"
}SEQ 102prompt.builtinfo{
"status": "completed"
}SEQ 103provider.requestedinfo{
"isolation_policy_hash": "57a5d1075866fc1fb1342e15d6029eaf80e0425db98f62a0cef0a56f6bd5f1e0",
"status": "running"
}SEQ 104provider.deltainfo{
"text": "{\"sql\":\"WITH monthly AS (\\n SELECT\\n strftime(fact_orders.order_date, '%Y-%m') AS month,\\n SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount) AS monthly_revenue\\n FROM fact_orders\\n JOIN fact_order_items\\n ON fact_order_items.order_id = fact_orders.order_id\\n WHERE fact_orders.status = 'completed'\\n AND fact_orders.order_date >= DATE '2025-01-01'\\n AND fact_orders.order_date < DATE '2026-01-01'\\n GROUP BY 1\\n), with_previous AS (\\n SELECT\\n month,\\n monthly_revenue,\\n LAG(monthly_revenue) OVER (ORDER BY month) AS previous_revenue\\n FROM monthly\\n)\\nSELECT\\n month,\\n monthly_revenue,\\n ROUND((monthly_revenue - previous_revenue) * 100.0 / NULLIF(previous_revenue, 0), 2) AS mom_pct\\nFROM with_previous\\nORDER BY month ASC;\",\"summary\":\"按月汇总 2025 年完成订单的净销售额，并使用 LAG 计算环比百分比；首个有收入月份及上月收入为 0 时环比为 NULL。\",\"assumptions\":[\"收入采用语义层定义的完成订单商品行净销售额。\",\"仅输出 2025 年存在完成订单收入记录的月份，不补齐无记录月份。\"]}"
}SEQ 105provider.completedinfo{
"elapsed_ms": 24382.355583999015,
"status": "completed",
"token_usage": {
"cache_write_input_tokens": 0,
"cached_input_tokens": 0,
"input_tokens": 19138,
"output_tokens": 514,
"reasoning_output_tokens": 187
}
}SEQ 106sql.parsedinfo{
"status": "completed"
}SEQ 107score.completedinfo{
"score": 100,
"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": "721ba5e54f56af1da456da0c6d8cbd3e3bc9e4ee240f91f7f0bcdd347940cae3",
"assumptions": null,
"attempt": 1,
"category": "window_offset",
"comparison": {
"abs_tolerance": "0.005",
"decimal_scale": 2,
"duplicate_policy": "multiset",
"max_rows": 10000,
"rel_tolerance": "0",
"row_order_significant": true
},
"difficulty": "medium",
"error_code": null,
"error_message": null,
"execution_ms": 203.9230000009411,
"expected_digest": "fccf4e8346d7523eb82d754179d442eacaffefa2dc0c4dfd3de2bf71f5a99ec0",
"expected_result_preview": {
"columns": [
{
"name": "month",
"type": "VARCHAR"
},
{
"name": "monthly_revenue",
"type": "DECIMAL(38,2)"
},
{
"name": "mom_pct",
"type": "DOUBLE"
}
],
"digest": "fccf4e8346d7523eb82d754179d442eacaffefa2dc0c4dfd3de2bf71f5a99ec0",
"row_count": 12,
"rows": [
[
"2025-01",
"41876.75",
null
],
[
"2025-02",
"31673.25",
"-24.37"
],
[
"2025-03",
"34373.25",
"8.52"
],
[
"2025-04",
"25069.00",
"-27.07"
],
[
"2025-05",
"30064.50",
"19.93"
],
[
"2025-06",
"31993.75",
"6.42"
],
[
"2025-07",
"36093.75",
"12.82"
],
[
"2025-08",
"29252.75",
"-18.95"
],
[
"2025-09",
"26246.25",
"-10.28"
],
[
"2025-10",
"21555.25",
"-17.87"
],
[
"2025-11",
"34944.25",
"62.11"
],
[
"2025-12",
"20373.00",
"-41.7"
]
]
},
"finished_at": "2026-08-29T16:40:26.468798",
"formatted_sql": "WITH monthly AS (\n SELECT\n STRFTIME(fact_orders.order_date, '%Y-%m') AS month,\n SUM(\n fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount\n ) AS monthly_revenue\n FROM fact_orders\n JOIN fact_order_items\n ON fact_order_items.order_id = fact_orders.order_id\n WHERE\n fact_orders.status = 'completed'\n AND fact_orders.order_date >= CAST('2025-01-01' AS DATE)\n AND fact_orders.order_date < CAST('2026-01-01' AS DATE)\n GROUP BY\n 1\n), with_previous AS (\n SELECT\n month,\n monthly_revenue,\n LAG(monthly_revenue) OVER (ORDER BY month) AS previous_revenue\n FROM monthly\n)\nSELECT\n month,\n monthly_revenue,\n ROUND((\n monthly_revenue - previous_revenue\n ) * 100.0 / NULLIF(previous_revenue, 0), 2) AS mom_pct\nFROM with_previous\nORDER BY\n month ASC",
"generated_sql": "WITH monthly AS (\n SELECT\n strftime(fact_orders.order_date, '%Y-%m') AS month,\n SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount) AS monthly_revenue\n FROM fact_orders\n JOIN fact_order_items\n ON fact_order_items.order_id = fact_orders.order_id\n WHERE fact_orders.status = 'completed'\n AND fact_orders.order_date >= DATE '2025-01-01'\n AND fact_orders.order_date < DATE '2026-01-01'\n GROUP BY 1\n), with_previous AS (\n SELECT\n month,\n monthly_revenue,\n LAG(monthly_revenue) OVER (ORDER BY month) AS previous_revenue\n FROM monthly\n)\nSELECT\n month,\n monthly_revenue,\n ROUND((monthly_revenue - previous_revenue) * 100.0 / NULLIF(previous_revenue, 0), 2) AS mom_pct\nFROM with_previous\nORDER BY month ASC;",
"generation_ms": null,
"id": 44,
"model_name": "Sol 本机实测",
"model_run_id": 13,
"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输出 2025 年每月收入及环比百分比 month、monthly_revenue、mom_pct，首月为 NULL，其余四舍五入 2 位，按 month 升序；必须使用 LAG。\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": "输出 2025 年每月收入及环比百分比 month、monthly_revenue、mom_pct，首月为 NULL，其余四舍五入 2 位，按 month 升序；必须使用 LAG。",
"radar_dimension": "窗口函数",
"raw_output": "{\"sql\":\"WITH monthly AS (\\n SELECT\\n strftime(fact_orders.order_date, '%Y-%m') AS month,\\n SUM(fact_order_items.quantity * fact_order_items.unit_price - fact_order_items.discount_amount) AS monthly_revenue\\n FROM fact_orders\\n JOIN fact_order_items\\n ON fact_order_items.order_id = fact_orders.order_id\\n WHERE fact_orders.status = 'completed'\\n AND fact_orders.order_date >= DATE '2025-01-01'\\n AND fact_orders.order_date < DATE '2026-01-01'\\n GROUP BY 1\\n), with_previous AS (\\n SELECT\\n month,\\n monthly_revenue,\\n LAG(monthly_revenue) OVER (ORDER BY month) AS previous_revenue\\n FROM monthly\\n)\\nSELECT\\n month,\\n monthly_revenue,\\n ROUND((monthly_revenue - previous_revenue) * 100.0 / NULLIF(previous_revenue, 0), 2) AS mom_pct\\nFROM with_previous\\nORDER BY month ASC;\",\"summary\":\"按月汇总 2025 年完成订单的净销售额，并使用 LAG 计算环比百分比；首个有收入月份及上月收入为 0 时环比为 NULL。\",\"assumptions\":[\"收入采用语义层定义的完成订单商品行净销售额。\",\"仅输出 2025 年存在完成订单收入记录的月份，不补齐无记录月份。\"]}",
"reference_sql": "WITH monthly AS (SELECT strftime(order_date, '%Y-%m') AS month, SUM(total_amount) AS monthly_revenue FROM fact_orders WHERE status = 'completed' AND order_date >= DATE '2025-01-01' AND order_date < DATE '2026-01-01' GROUP BY month), lagged AS (SELECT month, monthly_revenue, LAG(monthly_revenue) OVER (ORDER BY month ASC) AS previous_revenue FROM monthly) SELECT month, monthly_revenue, ROUND((monthly_revenue - previous_revenue) / NULLIF(previous_revenue, 0) * 100, 2) AS mom_pct FROM lagged ORDER BY month ASC",
"requested_model_id": "gpt-5.6-sol",
"required_ast": [
{
"id": "mom-lag",
"kind": "window_function",
"min": 1,
"name": "LAG",
"order_by": [
{
"direction": "ASC",
"expression": "month"
}
],
"partition_columns": []
}
],
"resolved_model_id": "gpt-5.6-sol",
"result_preview": {
"columns": [
{
"name": "month",
"type": "VARCHAR"
},
{
"name": "monthly_revenue",
"type": "DECIMAL(38,2)"
},
{
"name": "mom_pct",
"type": "DOUBLE"
}
],
"extra": [],
"missing": [],
"row_count": 12,
"rows": [
[
"2025-01",
"41876.75",
null
],
[
"2025-02",
"31673.25",
-24.37
],
[
"2025-03",
"34373.25",
8.52
],
[
"2025-04",
"25069.00",
-27.07
],
[
"2025-05",
"30064.50",
19.93
],
[
"2025-06",
"31993.75",
6.42
],
[
"2025-07",
"36093.75",
12.82
],
[
"2025-08",
"29252.75",
-18.95
],
[
"2025-09",
"26246.25",
-10.28
],
[
"2025-10",
"21555.25",
-17.87
],
[
"2025-11",
"34944.25",
62.11
],
[
"2025-12",
"20373.00",
-41.7
]
]
},
"run_id": 12,
"score": {
"ast_rules": [
{
"details": {
"actual": 1,
"required": 1
},
"id": "mom-lag",
"kind": "window_function",
"passed": true
}
],
"column_count": 5,
"column_names": 5,
"execution": 10,
"ordering": 10,
"protocol": 5,
"read_only_ast": 5,
"row_f1": 45,
"sql_capability": 15,
"total": 100
},
"stable_key": "monthly_mom_growth",
"started_at": "2026-08-29T16:40:01.859280",
"status": "completed",
"suite_content_hash": "0a4a18b4374f510f5eff18b06272c30c3375e1f082ae405adc8ead7dd9c81556",
"title": "月收入环比增长",
"token_usage": {
"cache_write_input_tokens": 0,
"cached_input_tokens": 0,
"input_tokens": 19138,
"output_tokens": 514,
"reasoning_output_tokens": 187
},
"visible_summary": "按月汇总 2025 年完成订单的净销售额，并使用 LAG 计算环比百分比；首个有收入月份及上月收入为 0 时环比为 NULL。"
}