SQL 查询

让 Claude 生成和执行 SQL

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How to make SQL queries with Claude

In this notebook, we’ll explore how to use Claude to generate SQL queries based on natural language questions. We’ll set up a test database, provide the schema to Claude, and demonstrate how it can understand and translate human language into SQL queries.

Setup

First, let’s install the necessary libraries and setup our Anthropic client with our API key.

# Install the necessary libraries
%pip install anthropic

# Import the required libraries
import sqlite3

from anthropic import Anthropic

# Set up the Claude API client
client = Anthropic()
MODEL_NAME = "claude-opus-4-1"

Creating a Test Database

We’ll create a test database using SQLite and populate it with sample data:

# Connect to the test database (or create it if it doesn't exist)
conn = sqlite3.connect("test_db.db")
cursor = conn.cursor()

# Create a sample table
cursor.execute("""
    CREATE TABLE IF NOT EXISTS employees (
        id INTEGER PRIMARY KEY,
        name TEXT,
        department TEXT,
        salary INTEGER
    )
""")

# Insert sample data
sample_data = [
    (1, "John Doe", "Sales", 50000),
    (2, "Jane Smith", "Engineering", 75000),
    (3, "Mike Johnson", "Sales", 60000),
    (4, "Emily Brown", "Engineering", 80000),
    (5, "David Lee", "Marketing", 55000),
]
cursor.executemany("INSERT INTO employees VALUES (?, ?, ?, ?)", sample_data)
conn.commit()

Generating SQL Queries with Claude

Now, let’s define a function to send a natural language question to Claude and get the generated SQL query:

# Define a function to send a query to Claude and get the response
def ask_claude(query, schema):
    prompt = f"""Here is the schema for a database:

{schema}

Given this schema, can you output a SQL query to answer the following question? Only output the SQL query and nothing else.

Question: {query}
"""

    response = client.messages.create(
        model=MODEL_NAME, max_tokens=2048, messages=[{"role": "user", "content": prompt}]
    )
    return response.content[0].text

We’ll retrieve the database schema and format it as a string:

# Get the database schema
schema = cursor.execute("PRAGMA table_info(employees)").fetchall()
schema_str = (
    "CREATE TABLE EMPLOYEES (\n" + "\n".join([f"{col[1]} {col[2]}" for col in schema]) + "\n)"
)
print(schema_str)

CREATE TABLE EMPLOYEES (
id INTEGER
name TEXT
department TEXT
salary INTEGER
)

Now, let’s provide an example natural language question and send it to Claude:

# Example natural language question
question = "What are the names and salaries of employees in the Engineering department?"
# Send the question to Claude and get the SQL query
sql_query = ask_claude(question, schema_str)
print(sql_query)

SELECT name, salary
FROM EMPLOYEES
WHERE department = 'Engineering';

Executing the Generated SQL Query

Finally, we’ll execute the generated SQL query on our test database and print the results:

# Execute the SQL query and print the results
results = cursor.execute(sql_query).fetchall()

for row in results:
    print(row)

('Jane Smith', 75000)
('Emily Brown', 80000)

Don’t forget to close the database connection when you’re done:

# Close the database connection
conn.close()

Tip

中文读者提示：本章节的代码和输出为英文原文。如需理解具体实现细节，可参考上方中文导读和代码注释。如有疑问，欢迎在 GitHub Issues 讨论。