品类收入贡献占比

{
  "actual_digest": "a687de80c137753f5f37e70c6b923a74599f99b13b5083537941801eaf5b9cde",
  "assumptions": [],
  "attempt": 1,
  "category": "ratio_analysis",
  "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": 190.4214580063126,
  "expected_digest": "3260d032955e880f29b82ad2fc4380c216934968b83b4415240519d410f8212a",
  "expected_result_preview": {
    "columns": [
      {
        "name": "category",
        "type": "VARCHAR"
      },
      {
        "name": "net_revenue",
        "type": "DECIMAL(38,2)"
      },
      {
        "name": "revenue_share_pct",
        "type": "DOUBLE"
      }
    ],
    "digest": "3260d032955e880f29b82ad2fc4380c216934968b83b4415240519d410f8212a",
    "row_count": 6,
    "rows": [
      [
        "品类-6",
        "173783.50",
        "31.09"
      ],
      [
        "品类-5",
        "115932.75",
        "20.74"
      ],
      [
        "品类-4",
        "103119.00",
        "18.45"
      ],
      [
        "品类-3",
        "81588.75",
        "14.6"
      ],
      [
        "品类-2",
        "56692.25",
        "10.14"
      ],
      [
        "品类-1",
        "27832.00",
        "4.98"
      ]
    ]
  },
  "finished_at": "2026-08-29T22:54:03.606558",
  "formatted_sql": "WITH category_revenue AS (\n  SELECT\n    p.category,\n    SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS net_revenue\n  FROM fact_order_items AS oi\n  INNER JOIN fact_orders AS o\n    ON oi.order_id = o.order_id\n  INNER JOIN dim_products AS p\n    ON oi.product_id = p.product_id\n  WHERE\n    o.status = 'completed'\n  GROUP BY\n    p.category\n), revenue_shares AS (\n  SELECT\n    category,\n    net_revenue,\n    ROUND(100 * net_revenue / NULLIF(SUM(net_revenue) OVER (), 0), 2) AS revenue_share_pct\n  FROM category_revenue\n)\nSELECT\n  category,\n  net_revenue,\n  revenue_share_pct\nFROM revenue_shares\nORDER BY\n  revenue_share_pct DESC,\n  category ASC",
  "generated_sql": "WITH category_revenue AS (\n    SELECT\n        p.category,\n        SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS net_revenue\n    FROM fact_order_items AS oi\n    INNER JOIN fact_orders AS o\n        ON oi.order_id = o.order_id\n    INNER JOIN dim_products AS p\n        ON oi.product_id = p.product_id\n    WHERE o.status = 'completed'\n    GROUP BY p.category\n), revenue_shares AS (\n    SELECT\n        category,\n        net_revenue,\n        ROUND(\n            100 * net_revenue / NULLIF(SUM(net_revenue) OVER (), 0),\n            2\n        ) AS revenue_share_pct\n    FROM category_revenue\n)\nSELECT\n    category,\n    net_revenue,\n    revenue_share_pct\nFROM revenue_shares\nORDER BY revenue_share_pct DESC NULLS LAST, category ASC",
  "generation_ms": null,
  "id": 162,
  "model_name": "Sol 本机实测",
  "model_run_id": 22,
  "plan": {
    "filters": [
      "fact_orders.status = 'completed'"
    ],
    "grain": "每个商品品类一行",
    "joins": [
      "fact_order_items.order_id = fact_orders.order_id",
      "fact_order_items.product_id = dim_products.product_id"
    ],
    "metrics": [
      "net_revenue = SUM(quantity * unit_price - discount_amount)",
      "revenue_share_pct = ROUND(net_revenue / 全部品类net_revenue * 100, 2)"
    ],
    "risks": [
      "全部品类净销售额为零时占比返回NULL",
      "负净销售额可能产生负占比"
    ],
    "sources": [
      "fact_orders",
      "fact_order_items",
      "dim_products"
    ],
    "steps": [
      "按品类汇总完成订单行净销售额",
      "用窗口汇总全部品类净销售额并计算占比",
      "按占比降序、品类升序排序"
    ]
  },
  "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按语义层净销售额口径计算每个品类的 net_revenue 和占全部品类净销售额的 revenue_share_pct，百分比四舍五入 2 位；按 revenue_share_pct 降序、category 升序。\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": "按语义层净销售额口径计算每个品类的 net_revenue 和占全部品类净销售额的 revenue_share_pct，百分比四舍五入 2 位；按 revenue_share_pct 降序、category 升序。",
  "radar_dimension": "聚合与指标",
  "raw_output": "{\"plan\":{\"grain\":\"每个商品品类一行\",\"sources\":[\"fact_orders\",\"fact_order_items\",\"dim_products\"],\"joins\":[\"fact_order_items.order_id = fact_orders.order_id\",\"fact_order_items.product_id = dim_products.product_id\"],\"filters\":[\"fact_orders.status = 'completed'\"],\"metrics\":[\"net_revenue = SUM(quantity * unit_price - discount_amount)\",\"revenue_share_pct = ROUND(net_revenue / 全部品类net_revenue * 100, 2)\"],\"steps\":[\"按品类汇总完成订单行净销售额\",\"用窗口汇总全部品类净销售额并计算占比\",\"按占比降序、品类升序排序\"],\"risks\":[\"全部品类净销售额为零时占比返回NULL\",\"负净销售额可能产生负占比\"]},\"sql\":\"WITH category_revenue AS (\\n    SELECT\\n        p.category,\\n        SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS net_revenue\\n    FROM fact_order_items AS oi\\n    INNER JOIN fact_orders AS o\\n        ON oi.order_id = o.order_id\\n    INNER JOIN dim_products AS p\\n        ON oi.product_id = p.product_id\\n    WHERE o.status = 'completed'\\n    GROUP BY p.category\\n), revenue_shares AS (\\n    SELECT\\n        category,\\n        net_revenue,\\n        ROUND(\\n            100 * net_revenue / NULLIF(SUM(net_revenue) OVER (), 0),\\n            2\\n        ) AS revenue_share_pct\\n    FROM category_revenue\\n)\\nSELECT\\n    category,\\n    net_revenue,\\n    revenue_share_pct\\nFROM revenue_shares\\nORDER BY revenue_share_pct DESC NULLS LAST, category ASC\",\"summary\":\"按商品品类汇总完成订单行的净销售额，再以全部品类净销售额为分母计算百分比占比并四舍五入到2位。\",\"assumptions\":[]}",
  "reference_sql": "WITH category_revenue AS (SELECT p.category, SUM(i.quantity * i.unit_price - i.discount_amount) AS net_revenue 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), total_revenue AS (SELECT SUM(net_revenue) AS total_value FROM category_revenue) SELECT c.category, c.net_revenue, ROUND(c.net_revenue / NULLIF(t.total_value, 0) * 100, 2) AS revenue_share_pct FROM category_revenue c CROSS JOIN total_revenue t ORDER BY revenue_share_pct DESC, c.category ASC",
  "requested_model_id": "gpt-5.6-sol",
  "required_ast": [
    {
      "id": "revenue-share-ctes",
      "kind": "cte_count",
      "min": 2
    }
  ],
  "resolved_model_id": "gpt-5.6-sol",
  "result_preview": {
    "columns": [
      {
        "name": "category",
        "type": "VARCHAR"
      },
      {
        "name": "net_revenue",
        "type": "DECIMAL(38,2)"
      },
      {
        "name": "revenue_share_pct",
        "type": "DOUBLE"
      }
    ],
    "extra": [],
    "missing": [],
    "row_count": 6,
    "rows": [
      [
        "品类-6",
        "173783.50",
        31.09
      ],
      [
        "品类-5",
        "115932.75",
        20.74
      ],
      [
        "品类-4",
        "103119.00",
        18.45
      ],
      [
        "品类-3",
        "81588.75",
        14.6
      ],
      [
        "品类-2",
        "56692.25",
        10.14
      ],
      [
        "品类-1",
        "27832.00",
        4.98
      ]
    ]
  },
  "run_id": 18,
  "score": {
    "ast_rules": [
      {
        "details": {
          "actual": 2,
          "required": 2
        },
        "id": "revenue-share-ctes",
        "kind": "cte_count",
        "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": "category_revenue_share",
  "started_at": "2026-08-29T22:53:42.351124",
  "status": "completed",
  "suite_content_hash": "5b5d98876ea35114f18ce6dfa48cc9800d88b6baba80d311b2f52552a38b31af",
  "title": "品类收入贡献占比",
  "token_usage": {
    "cache_write_input_tokens": 0,
    "cached_input_tokens": 9984,
    "input_tokens": 19518,
    "output_tokens": 426,
    "reasoning_output_tokens": 0
  },
  "visible_summary": "按商品品类汇总完成订单行的净销售额，再以全部品类净销售额为分母计算百分比占比并四舍五入到2位。"
}