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ai-agent-book/chapter5/erp-agent/validation/latest.json
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ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）
2026-08-20 13:12:50 +00:00

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{
"schema_version": "1.0",
"experiment": "5-10",
"run_id": "20260729T210334Z-5_10-postgresql",
"started_at_utc": "2026-07-29T21:03:34.066223+00:00",
"completed_at_utc": "2026-07-29T21:06:43.050217+00:00",
"provider": "ark",
"endpoint": "https://ark.cn-beijing.volces.com/api/v3",
"model": "doubao-seed-1-6-250615",
"postgresql": {
"version": "PostgreSQL 14.13 (Homebrew) on aarch64-apple-darwin23.6.0, compiled by Apple clang version 16.0.0 (clang-1600.0.26.4), 64-bit",
"database": "postgres",
"schema": "exp5_10_20260729t210334z510postgresql",
"employees": 40,
"salary_rows": 1184
},
"source": {
"manuscript": "book/chapter5.md#实验-5-10",
"campaign_sha256": "d921a6d18683dcbcfa84f4ea08ad67a622315ddf54b05cf9a1ea048564eae209",
"seed_sha256": "833c1a09349802dfad70c84b7d568d1e06ec3d73bdef588c796b2d704a73730d"
},
"records": [
{
"id": 1,
"question": "平均每个员工在职多久？",
"sql": "SELECT AVG(COALESCE(leave_date, CURRENT_DATE) - hire_date) AS average_tenure_days FROM employees",
"sql_attempts": [
{
"attempt": 1,
"sql": "SELECT AVG(COALESCE(leave_date, CURRENT_DATE) - hire_date) AS average_tenure_days FROM employees",
"query_latency_s": 0.0015,
"execution_error": null
}
],
"rows": [
[
886.2
]
],
"row_count": 1,
"query_latency_s": 0.0015,
"expected": [
[
886.2
]
],
"passed": true,
"comparison": "independent Python reference matched"
},
{
"id": 2,
"question": "每个部门有多少在职员工？",
"sql": "SELECT department, COUNT(emp_id) AS active_count FROM employees WHERE leave_date IS NULL GROUP BY department",
"sql_attempts": [
{
"attempt": 1,
"sql": "SELECT department, COUNT(emp_id) AS active_count FROM employees WHERE leave_date IS NULL GROUP BY department",
"query_latency_s": 0.0007,
"execution_error": null
}
],
"rows": [
[
"人力资源部",
4
],
[
"市场部",
9
],
[
"研发部",
5
],
[
"财务部",
6
],
[
"销售部",
10
]
],
"row_count": 5,
"query_latency_s": 0.0007,
"expected": [
[
"研发部",
5
],
[
"销售部",
10
],
[
"人力资源部",
4
],
[
"市场部",
9
],
[
"财务部",
6
]
],
"passed": true,
"comparison": "independent Python reference matched"
},
{
"id": 3,
"question": "哪个部门员工平均级别最高？",
"sql": "SELECT department\nFROM employees\nGROUP BY department\nORDER BY AVG(level) DESC\nLIMIT 1",
"sql_attempts": [
{
"attempt": 1,
"sql": "SELECT department\nFROM employees\nGROUP BY department\nORDER BY AVG(level) DESC\nLIMIT 1",
"query_latency_s": 0.0008,
"execution_error": null
}
],
"rows": [
[
"销售部"
]
],
"row_count": 1,
"query_latency_s": 0.0008,
"expected": [
[
"销售部"
]
],
"passed": true,
"comparison": "independent Python reference matched"
},
{
"id": 4,
"question": "每个部门今年和去年各新入职多少人？",
"sql": "SELECT department,\n COUNT(*) FILTER (WHERE EXTRACT(YEAR FROM hire_date) = EXTRACT(YEAR FROM CURRENT_DATE)) AS \"this-year count\",\n COUNT(*) FILTER (WHERE EXTRACT(YEAR FROM hire_date) = EXTRACT(YEAR FROM CURRENT_DATE) - 1) AS \"last-year count\"\nFROM employees\nGROUP BY department\nHAVING COUNT(*) FILTER (WHERE EXTRACT(YEAR FROM hire_date) = EXTRACT(YEAR FROM CURRENT_DATE)) > 0\n OR COUNT(*) FILTER (WHERE EXTRACT(YEAR FROM hire_date) = EXTRACT(YEAR FROM CURRENT_DATE) - 1) > 0",
"sql_attempts": [
{
"attempt": 1,
"sql": "SELECT department,\n COUNT(*) FILTER (WHERE EXTRACT(YEAR FROM hire_date) = EXTRACT(YEAR FROM CURRENT_DATE)) AS \"this-year count\",\n COUNT(*) FILTER (WHERE EXTRACT(YEAR FROM hire_date) = EXTRACT(YEAR FROM CURRENT_DATE) - 1) AS \"last-year count\"\nFROM employees\nGROUP BY department\nHAVING COUNT(*) FILTER (WHERE EXTRACT(YEAR FROM hire_date) = EXTRACT(YEAR FROM CURRENT_DATE)) > 0\n OR COUNT(*) FILTER (WHERE EXTRACT(YEAR FROM hire_date) = EXTRACT(YEAR FROM CURRENT_DATE) - 1) > 0",
"query_latency_s": 0.0008,
"execution_error": null
}
],
"rows": [
[
"财务部",
0,
1
],
[
"销售部",
2,
1
],
[
"人力资源部",
2,
0
],
[
"研发部",
1,
2
],
[
"市场部",
0,
4
]
],
"row_count": 5,
"query_latency_s": 0.0008,
"expected": [
[
"研发部",
1,
2
],
[
"销售部",
2,
1
],
[
"人力资源部",
2,
0
],
[
"市场部",
0,
4
],
[
"财务部",
0,
1
]
],
"passed": true,
"comparison": "independent Python reference matched"
},
{
"id": 5,
"question": "前年3月到去年5月，A部门平均工资是多少？",
"sql": "SELECT AVG(s.salary)\nFROM employees e\nJOIN salaries s ON e.emp_id = s.emp_id\nWHERE e.department = '研发部'\n AND s.pay_date BETWEEN make_date(EXTRACT(YEAR FROM CURRENT_DATE)::INTEGER - 2, 3, 1)\n AND make_date(EXTRACT(YEAR FROM CURRENT_DATE)::INTEGER - 1, 5, 31)",
"sql_attempts": [
{
"attempt": 1,
"sql": "SELECT AVG(s.salary)\nFROM employees e\nJOIN salaries s ON e.emp_id = s.emp_id\nWHERE e.department = '研发部'\n AND s.pay_date BETWEEN make_date(EXTRACT(YEAR FROM CURRENT_DATE)::INTEGER - 2, 3, 1)\n AND make_date(EXTRACT(YEAR FROM CURRENT_DATE)::INTEGER - 1, 5, 31)",
"query_latency_s": 0.0021,
"execution_error": null
}
],
"rows": [
[
27695.833333333332
]
],
"row_count": 1,
"query_latency_s": 0.0021,
"expected": [
[
27695.83
]
],
"passed": true,
"comparison": "independent Python reference matched"
},
{
"id": 6,
"question": "去年A部门和B部门平均工资哪个高？",
"sql": "SELECT department, AVG(salary) AS average_salary\nFROM employees\nJOIN salaries ON employees.emp_id = salaries.emp_id\nWHERE department IN ('研发部', '销售部')\n AND pay_date >= date_trunc('year', CURRENT_DATE) - INTERVAL '1 year'\n AND pay_date < date_trunc('year', CURRENT_DATE)\nGROUP BY department",
"sql_attempts": [
{
"attempt": 1,
"sql": "SELECT department, AVG(salary) AS average_salary\nFROM employees\nJOIN salaries ON employees.emp_id = salaries.emp_id\nWHERE department IN ('研发部', '销售部')\n AND pay_date >= date_trunc('year', CURRENT_DATE) - INTERVAL '1 year'\n AND pay_date < date_trunc('year', CURRENT_DATE)\nGROUP BY department",
"query_latency_s": 0.0011,
"execution_error": null
}
],
"rows": [
[
"研发部",
28569.444444444445
],
[
"销售部",
29481.55339805825
]
],
"row_count": 2,
"query_latency_s": 0.0011,
"expected": [
[
"研发部",
28569.44
],
[
"销售部",
29481.55
]
],
"passed": true,
"comparison": "independent Python reference matched"
},
{
"id": 7,
"question": "今年每个级别的员工平均工资是多少？",
"sql": "SELECT e.level, AVG(s.salary) AS average_salary\nFROM employees e\nJOIN salaries s ON e.emp_id = s.emp_id\nWHERE EXTRACT(YEAR FROM s.pay_date) = EXTRACT(YEAR FROM CURRENT_DATE)\nGROUP BY e.level",
"sql_attempts": [
{
"attempt": 1,
"sql": "SELECT e.level, AVG(s.salary) AS average_salary\nFROM employees e\nJOIN salaries s ON e.emp_id = s.emp_id\nWHERE EXTRACT(YEAR FROM s.pay_date) = EXTRACT(YEAR FROM CURRENT_DATE)\nGROUP BY e.level",
"query_latency_s": 0.0014,
"execution_error": null
}
],
"rows": [
[
3,
23456.428571428572
],
[
4,
26248.823529411766
],
[
5,
34534.75
],
[
6,
28461.363636363636
],
[
7,
29935.0
],
[
8,
32242.69230769231
],
[
9,
34559.90566037736
]
],
"row_count": 7,
"query_latency_s": 0.0014,
"expected": [
[
3,
23456.43
],
[
8,
32242.69
],
[
6,
28461.36
],
[
4,
26248.82
],
[
9,
34559.91
],
[
5,
34534.75
],
[
7,
29935
]
],
"passed": true,
"comparison": "independent Python reference matched"
},
{
"id": 8,
"question": "入职一年内、一到两年、两到三年的员工，最近一个月平均工资是多少？",
"sql": "WITH latest_salaries AS (\n SELECT DISTINCT ON (emp_id) emp_id, salary\n FROM salaries\n ORDER BY emp_id, pay_date DESC\n)\nSELECT \n CASE \n WHEN (CURRENT_DATE - e.hire_date)::INTEGER < 365 THEN '入职一年内'\n WHEN (CURRENT_DATE - e.hire_date)::INTEGER BETWEEN 365 AND 729 THEN '一到两年'\n WHEN (CURRENT_DATE - e.hire_date)::INTEGER BETWEEN 730 AND 1094 THEN '两到三年'\n END AS bucket,\n AVG(ls.salary) AS average_latest_salary\nFROM employees e\nJOIN latest_salaries ls ON e.emp_id = ls.emp_id\nWHERE e.leave_date IS NULL\n AND (CURRENT_DATE - e.hire_date)::INTEGER < 1095\nGROUP BY bucket\nORDER BY bucket",
"sql_attempts": [
{
"attempt": 1,
"sql": "WITH latest_salaries AS (\n SELECT DISTINCT ON (emp_id) emp_id, salary\n FROM salaries\n ORDER BY emp_id, pay_date DESC\n)\nSELECT \n CASE \n WHEN (CURRENT_DATE - e.hire_date)::INTEGER < 365 THEN '入职一年内'\n WHEN (CURRENT_DATE - e.hire_date)::INTEGER BETWEEN 365 AND 729 THEN '一到两年'\n WHEN (CURRENT_DATE - e.hire_date)::INTEGER BETWEEN 730 AND 1094 THEN '两到三年'\n END AS bucket,\n AVG(ls.salary) AS average_latest_salary\nFROM employees e\nJOIN latest_salaries ls ON e.emp_id = ls.emp_id\nWHERE e.leave_date IS NULL\n AND (CURRENT_DATE - e.hire_date)::INTEGER < 1095\nGROUP BY bucket\nORDER BY bucket",
"query_latency_s": 0.0015,
"execution_error": null
}
],
"rows": [
[
"一到两年",
27683.125
],
[
"两到三年",
31138.75
],
[
"入职一年内",
24053.75
]
],
"row_count": 3,
"query_latency_s": 0.0015,
"expected": [
[
"入职一年内",
24053.75
],
[
"一到两年",
27683.12
],
[
"两到三年",
31138.75
]
],
"passed": true,
"comparison": "independent Python reference matched"
},
{
"id": 9,
"question": "去年到今年涨薪幅度最大的10位员工是谁？",
"sql": "WITH emp_salary_avg AS (\n SELECT \n emp_id,\n AVG(salary) FILTER (WHERE EXTRACT(YEAR FROM pay_date) = EXTRACT(YEAR FROM CURRENT_DATE)) AS current_avg,\n AVG(salary) FILTER (WHERE EXTRACT(YEAR FROM pay_date) = EXTRACT(YEAR FROM CURRENT_DATE) - 1) AS previous_avg\n FROM salaries\n GROUP BY emp_id\n HAVING \n AVG(salary) FILTER (WHERE EXTRACT(YEAR FROM pay_date) = EXTRACT(YEAR FROM CURRENT_DATE)) IS NOT NULL\n AND AVG(salary) FILTER (WHERE EXTRACT(YEAR FROM pay_date) = EXTRACT(YEAR FROM CURRENT_DATE) - 1) IS NOT NULL\n)\nSELECT \n e.name,\n (esa.current_avg - esa.previous_avg) AS raise_amount\nFROM emp_salary_avg esa\nJOIN employees e ON esa.emp_id = e.emp_id\nORDER BY raise_amount DESC\nLIMIT 10",
"sql_attempts": [
{
"attempt": 1,
"sql": "WITH emp_salary_avg AS (\n SELECT \n emp_id,\n AVG(salary) FILTER (WHERE EXTRACT(YEAR FROM pay_date) = EXTRACT(YEAR FROM CURRENT_DATE)) AS current_avg,\n AVG(salary) FILTER (WHERE EXTRACT(YEAR FROM pay_date) = EXTRACT(YEAR FROM CURRENT_DATE) - 1) AS previous_avg\n FROM salaries\n GROUP BY emp_id\n HAVING \n AVG(salary) FILTER (WHERE EXTRACT(YEAR FROM pay_date) = EXTRACT(YEAR FROM CURRENT_DATE)) IS NOT NULL\n AND AVG(salary) FILTER (WHERE EXTRACT(YEAR FROM pay_date) = EXTRACT(YEAR FROM CURRENT_DATE) - 1) IS NOT NULL\n)\nSELECT \n e.name,\n (esa.current_avg - esa.previous_avg) AS raise_amount\nFROM emp_salary_avg esa\nJOIN employees e ON esa.emp_id = e.emp_id\nORDER BY raise_amount DESC\nLIMIT 10",
"query_latency_s": 0.0018,
"execution_error": null
}
],
"rows": [
[
"韩平",
12000.0
],
[
"韩伟",
2200.0
],
[
"吕芳",
2155.0
],
[
"金丽",
2110.0
],
[
"许华",
2065.0
],
[
"陶松",
1975.0
],
[
"周雪",
1930.0
],
[
"吴敏",
1885.0
],
[
"张霞",
1840.0
],
[
"赵平",
1795.0
]
],
"row_count": 10,
"query_latency_s": 0.0018,
"expected": [
[
"韩平",
12000
],
[
"韩伟",
2200
],
[
"吕芳",
2155
],
[
"金丽",
2110
],
[
"许华",
2065
],
[
"陶松",
1975
],
[
"周雪",
1930
],
[
"吴敏",
1885
],
[
"张霞",
1840
],
[
"赵平",
1795
]
],
"passed": true,
"comparison": "independent Python reference matched"
},
{
"id": 10,
"question": "有没有拖欠工资的情况（某个月还在职却没有发薪）？",
"sql": "WITH employee_employed_months AS (\n SELECT \n e.emp_id,\n generate_series(\n date_trunc('month', e.hire_date),\n date_trunc('month', COALESCE(e.leave_date, CURRENT_DATE)),\n interval '1 month'\n ) AS employed_month\n FROM employees e\n)\nSELECT \n em.emp_id,\n to_char(em.employed_month, 'YYYY-MM') AS month\nFROM employee_employed_months em\nLEFT JOIN salaries s \n ON em.emp_id = s.emp_id \n AND date_trunc('month', s.pay_date) = em.employed_month\nWHERE s.emp_id IS NULL",
"sql_attempts": [
{
"attempt": 1,
"sql": "WITH employee_employed_months AS (\n SELECT \n e.emp_id,\n generate_series(\n date_trunc('month', e.hire_date),\n date_trunc('month', COALESCE(e.leave_date, CURRENT_DATE)),\n interval '1 month'\n ) AS employed_month\n FROM employees e\n)\nSELECT \n em.emp_id,\n to_char(em.employed_month, 'YYYY-MM') AS month\nFROM employee_employed_months em\nLEFT JOIN salaries s \n ON em.emp_id = s.emp_id \n AND date_trunc('month', s.pay_date) = em.employed_month\nWHERE s.emp_id IS NULL",
"query_latency_s": 0.0122,
"execution_error": null
}
],
"rows": [
[
17,
"2026-01"
]
],
"row_count": 1,
"query_latency_s": 0.0122,
"expected": [
[
17,
"2026-01"
]
],
"passed": true,
"comparison": "independent Python reference matched"
}
],
"browser": {
"browser": "Chromium",
"version": "139.0.7258.5",
"html": "results.html",
"screenshot": "results.png"
},
"usage": {
"calls": 10,
"prompt_tokens": 3154,
"completion_tokens": 8192,
"total_tokens": 11346,
"model_latency_s": 187.73,
"db_latency_s": 0.0239
},
"artifacts": {
"employees.json": {
"path": "employees.json",
"sha256": "7e5e237da09edb3db029e367a52569267241673a99e19d2ffddbb85a45cb59bd"
},
"salaries.json": {
"path": "salaries.json",
"sha256": "70a9dcd335d5c3e277eac92a4ae351f0f33c56ef9b2a526047cf60c98ed04228"
},
"schema.sql": {
"path": "schema.sql",
"sha256": "76a7d9f85d8bf3998729c40fa448e61e49ec24d47acba2f04580e5d2aeb27596"
},
"receipts.json": {
"path": "receipts.json",
"sha256": "0eb53eb1ac08d3b4091d02db1053e67078ac83e0f564ba8ae6f76fbc937e7a85"
},
"queries_and_results.json": {
"path": "queries_and_results.json",
"sha256": "a2be9fa715a15fe4290ee250f67a1c742ff8a3c3cff215f664a0d77b10ec8b40"
},
"results.html": {
"path": "results.html",
"sha256": "6099929004da8eb22e56f2557d3e167cd39ae2aee2b4a70d2fe61576d1980639"
},
"results.png": {
"path": "results.png",
"sha256": "9dccf840df361ad4031eb559672639876595f038f907e95987cc5c7ca550462c"
}
},
"acceptance_gates": {
"real_postgresql_server": true,
"exact_two_table_schema_created": true,
"all_10_natural_language_questions_attempted": true,
"all_10_sql_artifacts_are_read_only": true,
"database_not_llm_received_rows": true,
"database_executed_every_artifact": true,
"all_10_answers_match_independent_reference": true,
"result_tables_rendered_directly_in_real_browser": true,
"raw_model_receipts_complete": true,
"repairs_use_execution_errors_only": true,
"raw_database_rows_and_hashes_retained": true
},
"official_complete": true
}
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