拾穗数据工作室SQL 评测报告与证据

案例记录 case-run-00161

运行 #18/semantic_channel_kpis

渠道核心指标

按 channel_type 输出 completed_order_count、net_revenue、paid_amount、refunded_amount、failed_amount。所有指标必须遵循语义层口径,避免订单行与支付记录直接连接造成重复计算,并按 channel_type 升序。

运行信息

以下字段来自本次运行的冻结记录。

run#18
case run#161
attempt1
statuscompleted
token total20,618
estimated cost不可估算
model generation未记录
SQL execution216ms
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 22:53
finished2026年8月29日 22:53
suite hash5b5d98876ea35114f18ce6dfa48cc9800d88b6baba80d311b2f52552a38b31af

模型输入

显示本次实际保存的完整 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。

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。","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"}]}

问题:
按 channel_type 输出 completed_order_count、net_revenue、paid_amount、refunded_amount、failed_amount。所有指标必须遵循语义层口径,避免订单行与支付记录直接连接造成重复计算,并按 channel_type 升序。

规划要求:
plan 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。
SQL 必须严格实现该 plan,并遵循语义层业务口径。
summary 只概括最终做法,assumptions 只列无法由上下文确定的必要假设。
输出必须严格匹配此 JSON Schema:
{"$defs":{"QueryPlan":{"additionalProperties":false,"properties":{"filters":{"items":{"type":"string"},"title":"Filters","type":"array"},"grain":{"title":"Grain","type":"string"},"joins":{"items":{"type":"string"},"title":"Joins","type":"array"},"metrics":{"items":{"type":"string"},"title":"Metrics","type":"array"},"risks":{"items":{"type":"string"},"title":"Risks","type":"array"},"sources":{"items":{"type":"string"},"title":"Sources","type":"array"},"steps":{"items":{"type":"string"},"title":"Steps","type":"array"}},"required":["grain","sources","joins","filters","metrics","steps","risks"],"title":"QueryPlan","type":"object"}},"additionalProperties":false,"properties":{"assumptions":{"items":{"type":"string"},"title":"Assumptions","type":"array"},"plan":{"$ref":"#/$defs/QueryPlan"},"sql":{"title":"Sql","type":"string"},"summary":{"title":"Summary","type":"string"}},"required":["plan","sql","summary","assumptions"],"title":"GenerationOutput","type":"object"}

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 125
{
  "isolation_policy_hash": "922fe6e1a33b0726bb5797d57755d8c5afd03a15fb6fffe56964e8f75389c3bc",
  "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
{"plan":{"grain":"每行一个 channel_type","sources":["dim_channels","fact_orders","fact_order_items","fact_payments"],"joins":["fact_orders.channel_id = dim_channels.channel_id","fact_order_items.order_id = fact_orders.order_id","fact_payments.order_id = fact_orders.order_id"],"filters":["订单数和净销售额仅含 fact_orders.status = 'completed'","支付金额仅按 fact_payments.status 分类"],"metrics":["completed_order_count:完成订单去重计数","net_revenue:完成订单行 quantity * unit_price - discount_amount 汇总","paid_amount:status = 'paid' 的支付金额","refunded_amount:status = 'refunded' 的支付金额","failed_amount:status = 'failed' 的支付金额"],"steps":["分别聚合完成订单数、订单行净销售额和支付状态金额","以 channel_type 汇总渠道维表","连接各指标聚合并将缺失值补零","按 channel_type 升序输出"],"risks":["订单行与支付记录不得直接连接","同一 channel_type 可对应多个 channel_id","支付指标不得套用完成订单过滤"]},"sql":"WITH channel_types AS (\n    SELECT channel_type\n    FROM dim_channels\n    GROUP BY channel_type\n),\ncompleted_orders AS (\n    SELECT\n        c.channel_type,\n        COUNT(DISTINCT o.order_id) AS completed_order_count\n    FROM fact_orders AS o\n    JOIN dim_channels AS c\n      ON o.channel_id = c.channel_id\n    WHERE o.status = 'completed'\n    GROUP BY c.channel_type\n),\nnet_revenue AS (\n    SELECT\n        c.channel_type,\n        SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS net_revenue\n    FROM fact_order_items AS oi\n    JOIN fact_orders AS o\n      ON oi.order_id = o.order_id\n    JOIN dim_channels AS c\n      ON o.channel_id = c.channel_id\n    WHERE o.status = 'completed'\n    GROUP BY c.channel_type\n),\npayment_amounts AS (\n    SELECT\n        c.channel_type,\n        SUM(CASE WHEN p.status = 'paid' THEN p.amount ELSE 0 END) AS paid_amount,\n        SUM(CASE WHEN p.status = 'refunded' THEN p.amount ELSE 0 END) AS refunded_amount,\n        SUM(CASE WHEN p.status = 'failed' THEN p.amount ELSE 0 END) AS failed_amount\n    FROM fact_payments AS p\n    JOIN fact_orders AS o\n      ON p.order_id = o.order_id\n    JOIN dim_channels AS c\n      ON o.channel_id = c.channel_id\n    GROUP BY c.channel_type\n)\nSELECT\n    ct.channel_type,\n    COALESCE(co.completed_order_count, 0) AS completed_order_count,\n    COALESCE(nr.net_revenue, 0) AS net_revenue,\n    COALESCE(pa.paid_amount, 0) AS paid_amount,\n    COALESCE(pa.refunded_amount, 0) AS refunded_amount,\n    COALESCE(pa.failed_amount, 0) AS failed_amount\nFROM channel_types AS ct\nLEFT JOIN completed_orders AS co\n  ON ct.channel_type = co.channel_type\nLEFT JOIN net_revenue AS nr\n  ON ct.channel_type = nr.channel_type\nLEFT JOIN payment_amounts AS pa\n  ON ct.channel_type = pa.channel_type\nORDER BY ct.channel_type ASC","summary":"分别在订单、订单行和支付粒度聚合指标,再按 channel_type 合并,避免订单行与支付记录相乘导致重复计算。","assumptions":["输出 dim_channels 中定义的所有 channel_type;没有对应事实记录的指标显示为 0。"]}
结构化查询方案 plan
{
  "filters": [
    "订单数和净销售额仅含 fact_orders.status = 'completed'",
    "支付金额仅按 fact_payments.status 分类"
  ],
  "grain": "每行一个 channel_type",
  "joins": [
    "fact_orders.channel_id = dim_channels.channel_id",
    "fact_order_items.order_id = fact_orders.order_id",
    "fact_payments.order_id = fact_orders.order_id"
  ],
  "metrics": [
    "completed_order_count:完成订单去重计数",
    "net_revenue:完成订单行 quantity * unit_price - discount_amount 汇总",
    "paid_amount:status = 'paid' 的支付金额",
    "refunded_amount:status = 'refunded' 的支付金额",
    "failed_amount:status = 'failed' 的支付金额"
  ],
  "risks": [
    "订单行与支付记录不得直接连接",
    "同一 channel_type 可对应多个 channel_id",
    "支付指标不得套用完成订单过滤"
  ],
  "sources": [
    "dim_channels",
    "fact_orders",
    "fact_order_items",
    "fact_payments"
  ],
  "steps": [
    "分别聚合完成订单数、订单行净销售额和支付状态金额",
    "以 channel_type 汇总渠道维表",
    "连接各指标聚合并将缺失值补零",
    "按 channel_type 升序输出"
  ]
}
模型生成 SQL
WITH channel_types AS (
    SELECT channel_type
    FROM dim_channels
    GROUP BY channel_type
),
completed_orders AS (
    SELECT
        c.channel_type,
        COUNT(DISTINCT o.order_id) AS completed_order_count
    FROM fact_orders AS o
    JOIN dim_channels AS c
      ON o.channel_id = c.channel_id
    WHERE o.status = 'completed'
    GROUP BY c.channel_type
),
net_revenue AS (
    SELECT
        c.channel_type,
        SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS net_revenue
    FROM fact_order_items AS oi
    JOIN fact_orders AS o
      ON oi.order_id = o.order_id
    JOIN dim_channels AS c
      ON o.channel_id = c.channel_id
    WHERE o.status = 'completed'
    GROUP BY c.channel_type
),
payment_amounts AS (
    SELECT
        c.channel_type,
        SUM(CASE WHEN p.status = 'paid' THEN p.amount ELSE 0 END) AS paid_amount,
        SUM(CASE WHEN p.status = 'refunded' THEN p.amount ELSE 0 END) AS refunded_amount,
        SUM(CASE WHEN p.status = 'failed' THEN p.amount ELSE 0 END) AS failed_amount
    FROM fact_payments AS p
    JOIN fact_orders AS o
      ON p.order_id = o.order_id
    JOIN dim_channels AS c
      ON o.channel_id = c.channel_id
    GROUP BY c.channel_type
)
SELECT
    ct.channel_type,
    COALESCE(co.completed_order_count, 0) AS completed_order_count,
    COALESCE(nr.net_revenue, 0) AS net_revenue,
    COALESCE(pa.paid_amount, 0) AS paid_amount,
    COALESCE(pa.refunded_amount, 0) AS refunded_amount,
    COALESCE(pa.failed_amount, 0) AS failed_amount
FROM channel_types AS ct
LEFT JOIN completed_orders AS co
  ON ct.channel_type = co.channel_type
LEFT JOIN net_revenue AS nr
  ON ct.channel_type = nr.channel_type
LEFT JOIN payment_amounts AS pa
  ON ct.channel_type = pa.channel_type
ORDER BY ct.channel_type ASC
规范化 SQL
WITH channel_types AS (
  SELECT
    channel_type
  FROM dim_channels
  GROUP BY
    channel_type
), completed_orders AS (
  SELECT
    c.channel_type,
    COUNT(DISTINCT o.order_id) AS completed_order_count
  FROM fact_orders AS o
  JOIN dim_channels AS c
    ON o.channel_id = c.channel_id
  WHERE
    o.status = 'completed'
  GROUP BY
    c.channel_type
), net_revenue AS (
  SELECT
    c.channel_type,
    SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS net_revenue
  FROM fact_order_items AS oi
  JOIN fact_orders AS o
    ON oi.order_id = o.order_id
  JOIN dim_channels AS c
    ON o.channel_id = c.channel_id
  WHERE
    o.status = 'completed'
  GROUP BY
    c.channel_type
), payment_amounts AS (
  SELECT
    c.channel_type,
    SUM(CASE WHEN p.status = 'paid' THEN p.amount ELSE 0 END) AS paid_amount,
    SUM(CASE WHEN p.status = 'refunded' THEN p.amount ELSE 0 END) AS refunded_amount,
    SUM(CASE WHEN p.status = 'failed' THEN p.amount ELSE 0 END) AS failed_amount
  FROM fact_payments AS p
  JOIN fact_orders AS o
    ON p.order_id = o.order_id
  JOIN dim_channels AS c
    ON o.channel_id = c.channel_id
  GROUP BY
    c.channel_type
)
SELECT
  ct.channel_type,
  COALESCE(co.completed_order_count, 0) AS completed_order_count,
  COALESCE(nr.net_revenue, 0) AS net_revenue,
  COALESCE(pa.paid_amount, 0) AS paid_amount,
  COALESCE(pa.refunded_amount, 0) AS refunded_amount,
  COALESCE(pa.failed_amount, 0) AS failed_amount
FROM channel_types AS ct
LEFT JOIN completed_orders AS co
  ON ct.channel_type = co.channel_type
LEFT JOIN net_revenue AS nr
  ON ct.channel_type = nr.channel_type
LEFT JOIN payment_amounts AS pa
  ON ct.channel_type = pa.channel_type
ORDER BY
  ct.channel_type ASC
Token 与耗时
{
  "token_usage": {
    "cache_write_input_tokens": 0,
    "cached_input_tokens": 0,
    "input_tokens": 19519,
    "output_tokens": 1099,
    "reasoning_output_tokens": 291
  },
  "generation_ms": null,
  "execution_ms": 215.86691700213123
}

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": 4,
        "required": 2
      },
      "id": "kpi-preaggregation",
      "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
}
参考 SQL
WITH sales AS (SELECT ch.channel_type, COUNT(DISTINCT o.order_id) AS completed_order_count, SUM(i.quantity * i.unit_price - i.discount_amount) AS net_revenue FROM dim_channels ch JOIN fact_orders o ON o.channel_id = ch.channel_id AND o.status = 'completed' JOIN fact_order_items i ON i.order_id = o.order_id GROUP BY ch.channel_type), payments AS (SELECT ch.channel_type, SUM(CASE WHEN p.status = 'paid' THEN p.amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN p.status = 'refunded' THEN p.amount ELSE 0 END) AS refunded_amount, SUM(CASE WHEN p.status = 'failed' THEN p.amount ELSE 0 END) AS failed_amount FROM dim_channels ch JOIN fact_orders o ON o.channel_id = ch.channel_id JOIN fact_payments p ON p.order_id = o.order_id GROUP BY ch.channel_type) SELECT ch.channel_type, COALESCE(s.completed_order_count, 0) AS completed_order_count, COALESCE(s.net_revenue, 0) AS net_revenue, COALESCE(p.paid_amount, 0) AS paid_amount, COALESCE(p.refunded_amount, 0) AS refunded_amount, COALESCE(p.failed_amount, 0) AS failed_amount FROM (SELECT DISTINCT channel_type FROM dim_channels) ch LEFT JOIN sales s ON s.channel_type = ch.channel_type LEFT JOIN payments p ON p.channel_type = ch.channel_type ORDER BY ch.channel_type ASC
AST 结构要求
[
  {
    "id": "kpi-preaggregation",
    "kind": "cte_count",
    "min": 2
  }
]
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": "channel_type",
      "type": "VARCHAR"
    },
    {
      "name": "completed_order_count",
      "type": "BIGINT"
    },
    {
      "name": "net_revenue",
      "type": "DECIMAL(38,2)"
    },
    {
      "name": "paid_amount",
      "type": "DECIMAL(38,2)"
    },
    {
      "name": "refunded_amount",
      "type": "DECIMAL(38,2)"
    },
    {
      "name": "failed_amount",
      "type": "DECIMAL(38,2)"
    }
  ],
  "digest": "c2e7ea06590c967caa9ecc5dd8be012a5ae8d46f9b6d6dbdea1d4b8e72a692e2",
  "row_count": 5,
  "rows": [
    [
      "affiliate",
      114,
      "129676.50",
      "130639.25",
      "5602.25",
      "13165.50"
    ],
    [
      "offline",
      112,
      "140491.50",
      "140754.00",
      "5162.75",
      "22860.50"
    ],
    [
      "organic",
      141,
      "145543.00",
      "134754.50",
      "8260.00",
      "24282.50"
    ],
    [
      "paid_search",
      117,
      "143237.25",
      "121154.50",
      "8709.75",
      "19564.25"
    ],
    [
      "partner",
      0,
      "0.00",
      "0.00",
      "0.00",
      "0.00"
    ]
  ]
}
实际结果预览
{
  "columns": [
    {
      "name": "channel_type",
      "type": "VARCHAR"
    },
    {
      "name": "completed_order_count",
      "type": "BIGINT"
    },
    {
      "name": "net_revenue",
      "type": "DECIMAL(38,2)"
    },
    {
      "name": "paid_amount",
      "type": "DECIMAL(38,2)"
    },
    {
      "name": "refunded_amount",
      "type": "DECIMAL(38,2)"
    },
    {
      "name": "failed_amount",
      "type": "DECIMAL(38,2)"
    }
  ],
  "extra": [],
  "missing": [],
  "row_count": 5,
  "rows": [
    [
      "affiliate",
      114,
      "129676.50",
      "130639.25",
      "5602.25",
      "13165.50"
    ],
    [
      "offline",
      112,
      "140491.50",
      "140754.00",
      "5162.75",
      "22860.50"
    ],
    [
      "organic",
      141,
      "145543.00",
      "134754.50",
      "8260.00",
      "24282.50"
    ],
    [
      "paid_search",
      117,
      "143237.25",
      "121154.50",
      "8709.75",
      "19564.25"
    ],
    [
      "partner",
      0,
      "0.00",
      "0.00",
      "0.00",
      "0.00"
    ]
  ]
}
摘要、错误与结果哈希
{
  "visible_summary": "分别在订单、订单行和支付粒度聚合指标,再按 channel_type 合并,避免订单行与支付记录相乘导致重复计算。",
  "error_code": null,
  "error_message": null,
  "expected_digest": "c2e7ea06590c967caa9ecc5dd8be012a5ae8d46f9b6d6dbdea1d4b8e72a692e2",
  "actual_digest": "e011205b29b762f8fcac07678b0ff932ec989049e2d79621e5e081ae7a1b067c",
  "assumptions": [
    "输出 dim_channels 中定义的所有 channel_type;没有对应事实记录的指标显示为 0。"
  ]
}

事件记录(8)

按全局事件序号排列,保留时间、级别、类型、消息和 payload。

SEQ 123case.startedinfo
{
  "status": "generating"
}
SEQ 124prompt.builtinfo
{
  "status": "completed"
}
SEQ 125provider.requestedinfo
{
  "isolation_policy_hash": "922fe6e1a33b0726bb5797d57755d8c5afd03a15fb6fffe56964e8f75389c3bc",
  "status": "running"
}
SEQ 126provider.deltainfo
{
  "text": "{\"plan\":{\"grain\":\"每行一个 channel_type\",\"sources\":[\"dim_channels\",\"fact_orders\",\"fact_order_items\",\"fact_payments\"],\"joins\":[\"fact_orders.channel_id = dim_channels.channel_id\",\"fact_order_items.order_id = fact_orders.order_id\",\"fact_payments.order_id = fact_orders.order_id\"],\"filters\":[\"订单数和净销售额仅含 fact_orders.status = 'completed'\",\"支付金额仅按 fact_payments.status 分类\"],\"metrics\":[\"completed_order_count:完成订单去重计数\",\"net_revenue:完成订单行 quantity * unit_price - discount_amount 汇总\",\"paid_amount:status = 'paid' 的支付金额\",\"refunded_amount:status = 'refunded' 的支付金额\",\"failed_amount:status = 'failed' 的支付金额\"],\"steps\":[\"分别聚合完成订单数、订单行净销售额和支付状态金额\",\"以 channel_type 汇总渠道维表\",\"连接各指标聚合并将缺失值补零\",\"按 channel_type 升序输出\"],\"risks\":[\"订单行与支付记录不得直接连接\",\"同一 channel_type 可对应多个 channel_id\",\"支付指标不得套用完成订单过滤\"]},\"sql\":\"WITH channel_types AS (\\n    SELECT channel_type\\n    FROM dim_channels\\n    GROUP BY channel_type\\n),\\ncompleted_orders AS (\\n    SELECT\\n        c.channel_type,\\n        COUNT(DISTINCT o.order_id) AS completed_order_count\\n    FROM fact_orders AS o\\n    JOIN dim_channels AS c\\n      ON o.channel_id = c.channel_id\\n    WHERE o.status = 'completed'\\n    GROUP BY c.channel_type\\n),\\nnet_revenue AS (\\n    SELECT\\n        c.channel_type,\\n        SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS net_revenue\\n    FROM fact_order_items AS oi\\n    JOIN fact_orders AS o\\n      ON oi.order_id = o.order_id\\n    JOIN dim_channels AS c\\n      ON o.channel_id = c.channel_id\\n    WHERE o.status = 'completed'\\n    GROUP BY c.channel_type\\n),\\npayment_amounts AS (\\n    SELECT\\n        c.channel_type,\\n        SUM(CASE WHEN p.status = 'paid' THEN p.amount ELSE 0 END) AS paid_amount,\\n        SUM(CASE WHEN p.status = 'refunded' THEN p.amount ELSE 0 END) AS refunded_amount,\\n        SUM(CASE WHEN p.status = 'failed' THEN p.amount ELSE 0 END) AS failed_amount\\n    FROM fact_payments AS p\\n    JOIN fact_orders AS o\\n      ON p.order_id = o.order_id\\n    JOIN dim_channels AS c\\n      ON o.channel_id = c.channel_id\\n    GROUP BY c.channel_type\\n)\\nSELECT\\n    ct.channel_type,\\n    COALESCE(co.completed_order_count, 0) AS completed_order_count,\\n    COALESCE(nr.net_revenue, 0) AS net_revenue,\\n    COALESCE(pa.paid_amount, 0) AS paid_amount,\\n    COALESCE(pa.refunded_amount, 0) AS refunded_amount,\\n    COALESCE(pa.failed_amount, 0) AS failed_amount\\nFROM channel_types AS ct\\nLEFT JOIN completed_orders AS co\\n  ON ct.channel_type = co.channel_type\\nLEFT JOIN net_revenue AS nr\\n  ON ct.channel_type = nr.channel_type\\nLEFT JOIN payment_amounts AS pa\\n  ON ct.channel_type = pa.channel_type\\nORDER BY ct.channel_type ASC\",\"summary\":\"分别在订单、订单行和支付粒度聚合指标,再按 channel_type 合并,避免订单行与支付记录相乘导致重复计算。\",\"assumptions\":[\"输出 dim_channels 中定义的所有 channel_type;没有对应事实记录的指标显示为 0。\"]}"
}
SEQ 127provider.completedinfo
{
  "elapsed_ms": 33212.904250001884,
  "status": "completed",
  "token_usage": {
    "cache_write_input_tokens": 0,
    "cached_input_tokens": 0,
    "input_tokens": 19519,
    "output_tokens": 1099,
    "reasoning_output_tokens": 291
  }
}
SEQ 128plan.completedinfo
{
  "grain": "每行一个 channel_type",
  "status": "completed",
  "steps": 4
}
SEQ 129sql.parsedinfo
{
  "status": "completed"
}
SEQ 130score.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": "e011205b29b762f8fcac07678b0ff932ec989049e2d79621e5e081ae7a1b067c",
  "assumptions": [
    "输出 dim_channels 中定义的所有 channel_type;没有对应事实记录的指标显示为 0。"
  ],
  "attempt": 1,
  "category": "semantic_metrics",
  "comparison": {
    "abs_tolerance": "0.005",
    "decimal_scale": 2,
    "duplicate_policy": "multiset",
    "max_rows": 10000,
    "rel_tolerance": "0",
    "row_order_significant": true
  },
  "difficulty": "hard",
  "error_code": null,
  "error_message": null,
  "execution_ms": 215.86691700213123,
  "expected_digest": "c2e7ea06590c967caa9ecc5dd8be012a5ae8d46f9b6d6dbdea1d4b8e72a692e2",
  "expected_result_preview": {
    "columns": [
      {
        "name": "channel_type",
        "type": "VARCHAR"
      },
      {
        "name": "completed_order_count",
        "type": "BIGINT"
      },
      {
        "name": "net_revenue",
        "type": "DECIMAL(38,2)"
      },
      {
        "name": "paid_amount",
        "type": "DECIMAL(38,2)"
      },
      {
        "name": "refunded_amount",
        "type": "DECIMAL(38,2)"
      },
      {
        "name": "failed_amount",
        "type": "DECIMAL(38,2)"
      }
    ],
    "digest": "c2e7ea06590c967caa9ecc5dd8be012a5ae8d46f9b6d6dbdea1d4b8e72a692e2",
    "row_count": 5,
    "rows": [
      [
        "affiliate",
        114,
        "129676.50",
        "130639.25",
        "5602.25",
        "13165.50"
      ],
      [
        "offline",
        112,
        "140491.50",
        "140754.00",
        "5162.75",
        "22860.50"
      ],
      [
        "organic",
        141,
        "145543.00",
        "134754.50",
        "8260.00",
        "24282.50"
      ],
      [
        "paid_search",
        117,
        "143237.25",
        "121154.50",
        "8709.75",
        "19564.25"
      ],
      [
        "partner",
        0,
        "0.00",
        "0.00",
        "0.00",
        "0.00"
      ]
    ]
  },
  "finished_at": "2026-08-29T22:53:42.348600",
  "formatted_sql": "WITH channel_types AS (\n  SELECT\n    channel_type\n  FROM dim_channels\n  GROUP BY\n    channel_type\n), completed_orders AS (\n  SELECT\n    c.channel_type,\n    COUNT(DISTINCT o.order_id) AS completed_order_count\n  FROM fact_orders AS o\n  JOIN dim_channels AS c\n    ON o.channel_id = c.channel_id\n  WHERE\n    o.status = 'completed'\n  GROUP BY\n    c.channel_type\n), net_revenue AS (\n  SELECT\n    c.channel_type,\n    SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS net_revenue\n  FROM fact_order_items AS oi\n  JOIN fact_orders AS o\n    ON oi.order_id = o.order_id\n  JOIN dim_channels AS c\n    ON o.channel_id = c.channel_id\n  WHERE\n    o.status = 'completed'\n  GROUP BY\n    c.channel_type\n), payment_amounts AS (\n  SELECT\n    c.channel_type,\n    SUM(CASE WHEN p.status = 'paid' THEN p.amount ELSE 0 END) AS paid_amount,\n    SUM(CASE WHEN p.status = 'refunded' THEN p.amount ELSE 0 END) AS refunded_amount,\n    SUM(CASE WHEN p.status = 'failed' THEN p.amount ELSE 0 END) AS failed_amount\n  FROM fact_payments AS p\n  JOIN fact_orders AS o\n    ON p.order_id = o.order_id\n  JOIN dim_channels AS c\n    ON o.channel_id = c.channel_id\n  GROUP BY\n    c.channel_type\n)\nSELECT\n  ct.channel_type,\n  COALESCE(co.completed_order_count, 0) AS completed_order_count,\n  COALESCE(nr.net_revenue, 0) AS net_revenue,\n  COALESCE(pa.paid_amount, 0) AS paid_amount,\n  COALESCE(pa.refunded_amount, 0) AS refunded_amount,\n  COALESCE(pa.failed_amount, 0) AS failed_amount\nFROM channel_types AS ct\nLEFT JOIN completed_orders AS co\n  ON ct.channel_type = co.channel_type\nLEFT JOIN net_revenue AS nr\n  ON ct.channel_type = nr.channel_type\nLEFT JOIN payment_amounts AS pa\n  ON ct.channel_type = pa.channel_type\nORDER BY\n  ct.channel_type ASC",
  "generated_sql": "WITH channel_types AS (\n    SELECT channel_type\n    FROM dim_channels\n    GROUP BY channel_type\n),\ncompleted_orders AS (\n    SELECT\n        c.channel_type,\n        COUNT(DISTINCT o.order_id) AS completed_order_count\n    FROM fact_orders AS o\n    JOIN dim_channels AS c\n      ON o.channel_id = c.channel_id\n    WHERE o.status = 'completed'\n    GROUP BY c.channel_type\n),\nnet_revenue AS (\n    SELECT\n        c.channel_type,\n        SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS net_revenue\n    FROM fact_order_items AS oi\n    JOIN fact_orders AS o\n      ON oi.order_id = o.order_id\n    JOIN dim_channels AS c\n      ON o.channel_id = c.channel_id\n    WHERE o.status = 'completed'\n    GROUP BY c.channel_type\n),\npayment_amounts AS (\n    SELECT\n        c.channel_type,\n        SUM(CASE WHEN p.status = 'paid' THEN p.amount ELSE 0 END) AS paid_amount,\n        SUM(CASE WHEN p.status = 'refunded' THEN p.amount ELSE 0 END) AS refunded_amount,\n        SUM(CASE WHEN p.status = 'failed' THEN p.amount ELSE 0 END) AS failed_amount\n    FROM fact_payments AS p\n    JOIN fact_orders AS o\n      ON p.order_id = o.order_id\n    JOIN dim_channels AS c\n      ON o.channel_id = c.channel_id\n    GROUP BY c.channel_type\n)\nSELECT\n    ct.channel_type,\n    COALESCE(co.completed_order_count, 0) AS completed_order_count,\n    COALESCE(nr.net_revenue, 0) AS net_revenue,\n    COALESCE(pa.paid_amount, 0) AS paid_amount,\n    COALESCE(pa.refunded_amount, 0) AS refunded_amount,\n    COALESCE(pa.failed_amount, 0) AS failed_amount\nFROM channel_types AS ct\nLEFT JOIN completed_orders AS co\n  ON ct.channel_type = co.channel_type\nLEFT JOIN net_revenue AS nr\n  ON ct.channel_type = nr.channel_type\nLEFT JOIN payment_amounts AS pa\n  ON ct.channel_type = pa.channel_type\nORDER BY ct.channel_type ASC",
  "generation_ms": null,
  "id": 161,
  "model_name": "Sol 本机实测",
  "model_run_id": 22,
  "plan": {
    "filters": [
      "订单数和净销售额仅含 fact_orders.status = 'completed'",
      "支付金额仅按 fact_payments.status 分类"
    ],
    "grain": "每行一个 channel_type",
    "joins": [
      "fact_orders.channel_id = dim_channels.channel_id",
      "fact_order_items.order_id = fact_orders.order_id",
      "fact_payments.order_id = fact_orders.order_id"
    ],
    "metrics": [
      "completed_order_count:完成订单去重计数",
      "net_revenue:完成订单行 quantity * unit_price - discount_amount 汇总",
      "paid_amount:status = 'paid' 的支付金额",
      "refunded_amount:status = 'refunded' 的支付金额",
      "failed_amount:status = 'failed' 的支付金额"
    ],
    "risks": [
      "订单行与支付记录不得直接连接",
      "同一 channel_type 可对应多个 channel_id",
      "支付指标不得套用完成订单过滤"
    ],
    "sources": [
      "dim_channels",
      "fact_orders",
      "fact_order_items",
      "fact_payments"
    ],
    "steps": [
      "分别聚合完成订单数、订单行净销售额和支付状态金额",
      "以 channel_type 汇总渠道维表",
      "连接各指标聚合并将缺失值补零",
      "按 channel_type 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按 channel_type 输出 completed_order_count、net_revenue、paid_amount、refunded_amount、failed_amount。所有指标必须遵循语义层口径,避免订单行与支付记录直接连接造成重复计算,并按 channel_type 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 channel_type 输出 completed_order_count、net_revenue、paid_amount、refunded_amount、failed_amount。所有指标必须遵循语义层口径,避免订单行与支付记录直接连接造成重复计算,并按 channel_type 升序。",
  "radar_dimension": "聚合与指标",
  "raw_output": "{\"plan\":{\"grain\":\"每行一个 channel_type\",\"sources\":[\"dim_channels\",\"fact_orders\",\"fact_order_items\",\"fact_payments\"],\"joins\":[\"fact_orders.channel_id = dim_channels.channel_id\",\"fact_order_items.order_id = fact_orders.order_id\",\"fact_payments.order_id = fact_orders.order_id\"],\"filters\":[\"订单数和净销售额仅含 fact_orders.status = 'completed'\",\"支付金额仅按 fact_payments.status 分类\"],\"metrics\":[\"completed_order_count:完成订单去重计数\",\"net_revenue:完成订单行 quantity * unit_price - discount_amount 汇总\",\"paid_amount:status = 'paid' 的支付金额\",\"refunded_amount:status = 'refunded' 的支付金额\",\"failed_amount:status = 'failed' 的支付金额\"],\"steps\":[\"分别聚合完成订单数、订单行净销售额和支付状态金额\",\"以 channel_type 汇总渠道维表\",\"连接各指标聚合并将缺失值补零\",\"按 channel_type 升序输出\"],\"risks\":[\"订单行与支付记录不得直接连接\",\"同一 channel_type 可对应多个 channel_id\",\"支付指标不得套用完成订单过滤\"]},\"sql\":\"WITH channel_types AS (\\n    SELECT channel_type\\n    FROM dim_channels\\n    GROUP BY channel_type\\n),\\ncompleted_orders AS (\\n    SELECT\\n        c.channel_type,\\n        COUNT(DISTINCT o.order_id) AS completed_order_count\\n    FROM fact_orders AS o\\n    JOIN dim_channels AS c\\n      ON o.channel_id = c.channel_id\\n    WHERE o.status = 'completed'\\n    GROUP BY c.channel_type\\n),\\nnet_revenue AS (\\n    SELECT\\n        c.channel_type,\\n        SUM(oi.quantity * oi.unit_price - oi.discount_amount) AS net_revenue\\n    FROM fact_order_items AS oi\\n    JOIN fact_orders AS o\\n      ON oi.order_id = o.order_id\\n    JOIN dim_channels AS c\\n      ON o.channel_id = c.channel_id\\n    WHERE o.status = 'completed'\\n    GROUP BY c.channel_type\\n),\\npayment_amounts AS (\\n    SELECT\\n        c.channel_type,\\n        SUM(CASE WHEN p.status = 'paid' THEN p.amount ELSE 0 END) AS paid_amount,\\n        SUM(CASE WHEN p.status = 'refunded' THEN p.amount ELSE 0 END) AS refunded_amount,\\n        SUM(CASE WHEN p.status = 'failed' THEN p.amount ELSE 0 END) AS failed_amount\\n    FROM fact_payments AS p\\n    JOIN fact_orders AS o\\n      ON p.order_id = o.order_id\\n    JOIN dim_channels AS c\\n      ON o.channel_id = c.channel_id\\n    GROUP BY c.channel_type\\n)\\nSELECT\\n    ct.channel_type,\\n    COALESCE(co.completed_order_count, 0) AS completed_order_count,\\n    COALESCE(nr.net_revenue, 0) AS net_revenue,\\n    COALESCE(pa.paid_amount, 0) AS paid_amount,\\n    COALESCE(pa.refunded_amount, 0) AS refunded_amount,\\n    COALESCE(pa.failed_amount, 0) AS failed_amount\\nFROM channel_types AS ct\\nLEFT JOIN completed_orders AS co\\n  ON ct.channel_type = co.channel_type\\nLEFT JOIN net_revenue AS nr\\n  ON ct.channel_type = nr.channel_type\\nLEFT JOIN payment_amounts AS pa\\n  ON ct.channel_type = pa.channel_type\\nORDER BY ct.channel_type ASC\",\"summary\":\"分别在订单、订单行和支付粒度聚合指标,再按 channel_type 合并,避免订单行与支付记录相乘导致重复计算。\",\"assumptions\":[\"输出 dim_channels 中定义的所有 channel_type;没有对应事实记录的指标显示为 0。\"]}",
  "reference_sql": "WITH sales AS (SELECT ch.channel_type, COUNT(DISTINCT o.order_id) AS completed_order_count, SUM(i.quantity * i.unit_price - i.discount_amount) AS net_revenue FROM dim_channels ch JOIN fact_orders o ON o.channel_id = ch.channel_id AND o.status = 'completed' JOIN fact_order_items i ON i.order_id = o.order_id GROUP BY ch.channel_type), payments AS (SELECT ch.channel_type, SUM(CASE WHEN p.status = 'paid' THEN p.amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN p.status = 'refunded' THEN p.amount ELSE 0 END) AS refunded_amount, SUM(CASE WHEN p.status = 'failed' THEN p.amount ELSE 0 END) AS failed_amount FROM dim_channels ch JOIN fact_orders o ON o.channel_id = ch.channel_id JOIN fact_payments p ON p.order_id = o.order_id GROUP BY ch.channel_type) SELECT ch.channel_type, COALESCE(s.completed_order_count, 0) AS completed_order_count, COALESCE(s.net_revenue, 0) AS net_revenue, COALESCE(p.paid_amount, 0) AS paid_amount, COALESCE(p.refunded_amount, 0) AS refunded_amount, COALESCE(p.failed_amount, 0) AS failed_amount FROM (SELECT DISTINCT channel_type FROM dim_channels) ch LEFT JOIN sales s ON s.channel_type = ch.channel_type LEFT JOIN payments p ON p.channel_type = ch.channel_type ORDER BY ch.channel_type ASC",
  "requested_model_id": "gpt-5.6-sol",
  "required_ast": [
    {
      "id": "kpi-preaggregation",
      "kind": "cte_count",
      "min": 2
    }
  ],
  "resolved_model_id": "gpt-5.6-sol",
  "result_preview": {
    "columns": [
      {
        "name": "channel_type",
        "type": "VARCHAR"
      },
      {
        "name": "completed_order_count",
        "type": "BIGINT"
      },
      {
        "name": "net_revenue",
        "type": "DECIMAL(38,2)"
      },
      {
        "name": "paid_amount",
        "type": "DECIMAL(38,2)"
      },
      {
        "name": "refunded_amount",
        "type": "DECIMAL(38,2)"
      },
      {
        "name": "failed_amount",
        "type": "DECIMAL(38,2)"
      }
    ],
    "extra": [],
    "missing": [],
    "row_count": 5,
    "rows": [
      [
        "affiliate",
        114,
        "129676.50",
        "130639.25",
        "5602.25",
        "13165.50"
      ],
      [
        "offline",
        112,
        "140491.50",
        "140754.00",
        "5162.75",
        "22860.50"
      ],
      [
        "organic",
        141,
        "145543.00",
        "134754.50",
        "8260.00",
        "24282.50"
      ],
      [
        "paid_search",
        117,
        "143237.25",
        "121154.50",
        "8709.75",
        "19564.25"
      ],
      [
        "partner",
        0,
        "0.00",
        "0.00",
        "0.00",
        "0.00"
      ]
    ]
  },
  "run_id": 18,
  "score": {
    "ast_rules": [
      {
        "details": {
          "actual": 4,
          "required": 2
        },
        "id": "kpi-preaggregation",
        "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": "semantic_channel_kpis",
  "started_at": "2026-08-29T22:53:08.881645",
  "status": "completed",
  "suite_content_hash": "5b5d98876ea35114f18ce6dfa48cc9800d88b6baba80d311b2f52552a38b31af",
  "title": "渠道核心指标",
  "token_usage": {
    "cache_write_input_tokens": 0,
    "cached_input_tokens": 0,
    "input_tokens": 19519,
    "output_tokens": 1099,
    "reasoning_output_tokens": 291
  },
  "visible_summary": "分别在订单、订单行和支付粒度聚合指标,再按 channel_type 合并,避免订单行与支付记录相乘导致重复计算。"
}