内容目标

• 学习如何使用 OpenAI 辅助生成和优化多表 SQL 查询
• 了解如何获取数据库结构信息并与 OpenAI 结合使用

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实操步骤

1. 创建 SQLite 数据库示例

创建数据库及表结构：

python">import sqlite3

# 连接 SQLite 数据库（如果不存在则创建）
conn = sqlite3.connect("company_data.db")
cursor = conn.cursor()

# 创建 employees 表
cursor.execute('''
CREATE TABLE IF NOT EXISTS employees (
    id INTEGER PRIMARY KEY,
    name TEXT,
    department_id INTEGER,
    salary REAL,
    hire_date TEXT
)
''')

# 创建 departments 表
cursor.execute('''
CREATE TABLE IF NOT EXISTS departments (
    id INTEGER PRIMARY KEY,
    name TEXT,
    budget REAL
)
''')

# 插入示例数据
cursor.executemany('''
INSERT OR IGNORE INTO employees (id, name, department_id, salary, hire_date)
VALUES (?, ?, ?, ?, ?)
''', [
    (1, "Alice", 1, 8500, "2022-03-15"),
    (2, "Bob", 2, 6200, "2023-05-01"),
    (3, "Charlie", 1, 9300, "2021-11-12"),
])

cursor.executemany('''
INSERT OR IGNORE INTO departments (id, name, budget)
VALUES (?, ?, ?)
''', [
    (1, "Engineering", 500000),
    (2, "HR", 150000)
])

conn.commit()
conn.close()
print("Database setup complete.")

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2. 自动读取数据库结构信息

使用 PRAGMA table_info() 查询元信息，以便将表结构传递给 OpenAI：

python">def get_table_info(db_name):
    conn = sqlite3.connect(db_name)
    cursor = conn.cursor()

    # 获取所有表名
    cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
    tables = cursor.fetchall()
    
    table_info = {}
    for table_name in tables:
        table_name = table_name[0]
        cursor.execute(f"PRAGMA table_info({table_name});")
        columns = cursor.fetchall()
        table_info[table_name] = [column[1] for column in columns]

    conn.close()
    return table_info

db_name = "company_data.db"
table_structure = get_table_info(db_name)
print("Database Structure:", table_structure)

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3. 生成两表关联查询

将数据库结构作为上下文传入 OpenAI，请求生成 SQL 查询：

python">import openai

# 设置 API 密钥
openai.api_key = "your-api-key"

# 构建提示信息
table_info_prompt = f"""
The database has the following structure:
Table `employees`: id, name, department_id, salary, hire_date
Table `departments`: id, name, budget
Write an SQL query to find the names of employees in the 'Engineering' department whose salary exceeds 8000.
The query should join the employees and departments tables.
"""

# 调用 OpenAI 生成 SQL 查询
response = openai.ChatCompletion.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": table_info_prompt}],
    max_tokens=150
)

sql_query = response['choices'][0]['message']['content']
print("Generated SQL Query:")
print(sql_query)

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4. 示例生成结果

SELECT e.name
FROM employees e
JOIN departments d ON e.department_id = d.id
WHERE d.name = 'Engineering' AND e.salary > 8000;

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小结

• 元信息读取：通过 PRAGMA table_info() 获取数据库表结构
• 查询生成：将表名、字段及业务规则传递给 OpenAI，可以生成跨表关联查询
• 应用场景：适用于复杂业务查询，如员工信息与部门预算的联动分析

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练习题

1. 实践查询生成：
   修改查询条件，让 OpenAI 生成一个查询语句，找出预算大于 300,000 且部门中员工平均工资超过 7000 的部门名称。

2. 优化查询：
   使用 OpenAI 请求优化生成的 SQL 查询，确保执行效率更高。