月收入环比增长

{
  "actual_digest": null,
  "assumptions": [
    "“2025 年每月”要求输出 2025 年全部 12 个月，无完成订单的月份收入记为 0。",
    "上月收入为 0 时环比百分比在数学上无法定义，因此返回 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": "external_access_forbidden",
  "error_message": "FROM 不允许表函数",
  "execution_ms": null,
  "expected_digest": null,
  "finished_at": "2026-08-29T22:07:34.784374",
  "formatted_sql": null,
  "generated_sql": "WITH calendar_months AS (\n    SELECT DATE '2025-01-01' + i * INTERVAL '1 month' AS month_start\n    FROM range(12) AS r(i)\n),\nmonthly_aggregates AS (\n    SELECT\n        date_trunc('month', o.order_date)::DATE AS month_start,\n        SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS monthly_revenue\n    FROM fact_orders AS o\n    INNER JOIN fact_order_items AS oi\n        ON oi.order_id = o.order_id\n    WHERE o.status = 'completed'\n      AND o.order_date >= DATE '2025-01-01'\n      AND o.order_date < DATE '2026-01-01'\n    GROUP BY 1\n),\nmonthly_series AS (\n    SELECT\n        c.month_start,\n        COALESCE(m.monthly_revenue, CAST(0 AS DECIMAL(38, 2))) AS monthly_revenue\n    FROM calendar_months AS c\n    LEFT JOIN monthly_aggregates AS m\n        ON m.month_start = c.month_start\n),\nwith_previous AS (\n    SELECT\n        month_start,\n        monthly_revenue,\n        LAG(monthly_revenue) OVER (ORDER BY month_start) AS previous_month_revenue\n    FROM monthly_series\n)\nSELECT\n    strftime(month_start, '%Y-%m') AS month,\n    monthly_revenue,\n    CASE\n        WHEN previous_month_revenue IS NULL OR previous_month_revenue = 0 THEN NULL\n        ELSE ROUND(\n            (monthly_revenue - previous_month_revenue) * 100.0\n            / previous_month_revenue,\n            2\n        )\n    END AS mom_pct\nFROM with_previous\nORDER BY month_start ASC;",
  "generation_ms": null,
  "id": 116,
  "model_name": "GPT 当前会话桥接（流程验收）",
  "model_run_id": 19,
  "plan": {
    "filters": [
      "fact_orders.status = 'completed'",
      "fact_orders.order_date >= DATE '2025-01-01' AND fact_orders.order_date < DATE '2026-01-01'"
    ],
    "grain": "2025 年每个日历月一行，共 12 行。",
    "joins": [
      "fact_order_items.order_id = fact_orders.order_id",
      "月历 LEFT JOIN 月度收入，保留无收入月份"
    ],
    "metrics": [
      "monthly_revenue = SUM(quantity * unit_price - discount_amount)，无收入月为 0",
      "mom_pct = (本月收入 - LAG(本月收入)) / LAG(本月收入) * 100，四舍五入 2 位"
    ],
    "risks": [
      "避免订单头 total_amount 替代订单行净销售额",
      "上月收入为 0 时环比无法定义，返回 NULL",
      "先按月聚合再计算 LAG，避免明细粒度导致重复"
    ],
    "sources": [
      "fact_orders",
      "fact_order_items",
      "DuckDB range(12) 生成 2025 月历"
    ],
    "steps": [
      "生成 2025 年 12 个月月历",
      "按订单月份汇总完成订单行净销售额",
      "补齐无收入月份并使用 LAG 取得上月收入",
      "计算环比百分比并按 month 升序输出"
    ]
  },
  "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": "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": "时间与窗口",
  "raw_output": "{\"plan\":{\"grain\":\"2025 年每个日历月一行，共 12 行。\",\"sources\":[\"fact_orders\",\"fact_order_items\",\"DuckDB range(12) 生成 2025 月历\"],\"joins\":[\"fact_order_items.order_id = fact_orders.order_id\",\"月历 LEFT JOIN 月度收入，保留无收入月份\"],\"filters\":[\"fact_orders.status = 'completed'\",\"fact_orders.order_date >= DATE '2025-01-01' AND fact_orders.order_date < DATE '2026-01-01'\"],\"metrics\":[\"monthly_revenue = SUM(quantity * unit_price - discount_amount)，无收入月为 0\",\"mom_pct = (本月收入 - LAG(本月收入)) / LAG(本月收入) * 100，四舍五入 2 位\"],\"steps\":[\"生成 2025 年 12 个月月历\",\"按订单月份汇总完成订单行净销售额\",\"补齐无收入月份并使用 LAG 取得上月收入\",\"计算环比百分比并按 month 升序输出\"],\"risks\":[\"避免订单头 total_amount 替代订单行净销售额\",\"上月收入为 0 时环比无法定义，返回 NULL\",\"先按月聚合再计算 LAG，避免明细粒度导致重复\"]},\"sql\":\"WITH calendar_months AS (\\n    SELECT DATE '2025-01-01' + i * INTERVAL '1 month' AS month_start\\n    FROM range(12) AS r(i)\\n),\\nmonthly_aggregates AS (\\n    SELECT\\n        date_trunc('month', o.order_date)::DATE AS month_start,\\n        SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS monthly_revenue\\n    FROM fact_orders AS o\\n    INNER JOIN fact_order_items AS oi\\n        ON oi.order_id = o.order_id\\n    WHERE o.status = 'completed'\\n      AND o.order_date >= DATE '2025-01-01'\\n      AND o.order_date < DATE '2026-01-01'\\n    GROUP BY 1\\n),\\nmonthly_series AS (\\n    SELECT\\n        c.month_start,\\n        COALESCE(m.monthly_revenue, CAST(0 AS DECIMAL(38, 2))) AS monthly_revenue\\n    FROM calendar_months AS c\\n    LEFT JOIN monthly_aggregates AS m\\n        ON m.month_start = c.month_start\\n),\\nwith_previous AS (\\n    SELECT\\n        month_start,\\n        monthly_revenue,\\n        LAG(monthly_revenue) OVER (ORDER BY month_start) AS previous_month_revenue\\n    FROM monthly_series\\n)\\nSELECT\\n    strftime(month_start, '%Y-%m') AS month,\\n    monthly_revenue,\\n    CASE\\n        WHEN previous_month_revenue IS NULL OR previous_month_revenue = 0 THEN NULL\\n        ELSE ROUND(\\n            (monthly_revenue - previous_month_revenue) * 100.0\\n            / previous_month_revenue,\\n            2\\n        )\\n    END AS mom_pct\\nFROM with_previous\\nORDER BY month_start ASC;\",\"summary\":\"按完成订单的订单行净额汇总 2025 年月收入，补齐 12 个日历月，并用 LAG 计算相邻月份的收入环比。\",\"assumptions\":[\"“2025 年每月”要求输出 2025 年全部 12 个月，无完成订单的月份收入记为 0。\",\"上月收入为 0 时环比百分比在数学上无法定义，因此返回 NULL。\"]}",
  "requested_model_id": "gpt-session-bridge",
  "required_ast": [
    {
      "id": "mom-lag",
      "kind": "window_function",
      "min": 1,
      "name": "LAG",
      "order_by": [
        {
          "direction": "ASC",
          "expression": "month"
        }
      ],
      "partition_columns": []
    }
  ],
  "resolved_model_id": "gpt-session-bridge",
  "result_preview": null,
  "run_id": 16,
  "score": {
    "ast_rules": [],
    "column_count": 0,
    "column_names": 0,
    "execution": 0,
    "ordering": 0,
    "protocol": 5,
    "read_only_ast": 0,
    "row_f1": 0,
    "sql_capability": 0,
    "total": 5
  },
  "stable_key": "monthly_mom_growth",
  "started_at": "2026-08-29T22:07:08.383144",
  "status": "failed",
  "suite_content_hash": "5b5d98876ea35114f18ce6dfa48cc9800d88b6baba80d311b2f52552a38b31af",
  "title": "月收入环比增长",
  "token_usage": {
    "completion_tokens": 562,
    "prompt_tokens": 2368,
    "total_tokens": 2930
  },
  "visible_summary": "按完成订单的订单行净额汇总 2025 年月收入，补齐 12 个日历月，并用 LAG 计算相邻月份的收入环比。"
}