SQL-Zauberer

Verwandle Alltagssprache in SQL-Abfragen.

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	Inhalt
System	Transformiere die folgenden natürlichsprachlichen Anfragen in gültige SQL-Abfragen. Nimm an, dass eine Datenbank mit den folgenden Tabellen und Spalten existiert: Customers: - customer_id (INT, PRIMARY KEY) - first_name (VARCHAR) - last_name (VARCHAR) - email (VARCHAR) - phone (VARCHAR) - address (VARCHAR) - city (VARCHAR) - state (VARCHAR) - zip_code (VARCHAR) Products: - product_id (INT, PRIMARY KEY) - product_name (VARCHAR) - description (TEXT) - category (VARCHAR) - price (DECIMAL) - stock_quantity (INT) Orders: - order_id (INT, PRIMARY KEY) - customer_id (INT, FOREIGN KEY REFERENCES Customers) - order_date (DATE) - total_amount (DECIMAL) - status (VARCHAR) Order_Items: - order_item_id (INT, PRIMARY KEY) - order_id (INT, FOREIGN KEY REFERENCES Orders) - product_id (INT, FOREIGN KEY REFERENCES Products) - quantity (INT) - price (DECIMAL) Reviews: - review_id (INT, PRIMARY KEY) - product_id (INT, FOREIGN KEY REFERENCES Products) - customer_id (INT, FOREIGN KEY REFERENCES Customers) - rating (INT) - comment (TEXT) - review_date (DATE) Employees: - employee_id (INT, PRIMARY KEY) - first_name (VARCHAR) - last_name (VARCHAR) - email (VARCHAR) - phone (VARCHAR) - hire_date (DATE) - job_title (VARCHAR) - department (VARCHAR) - salary (DECIMAL) Stelle die SQL-Abfrage bereit, die die Daten basierend auf der natürlichsprachlichen Anfrage abrufen würde.
User	Hole die Liste der Kunden, die Bestellungen aufgegeben haben, aber keine Bewertungen abgegeben haben, zusammen mit dem Gesamtbetrag, den sie für Bestellungen ausgegeben haben.

Beispielausgabe

SELECT c.first_name, c.last_name, SUM(o.total_amount) AS total_spent
FROM Customers c
INNER JOIN Orders o ON c.customer_id = o.customer_id
LEFT JOIN Reviews r ON c.customer_id = r.customer_id
WHERE r.review_id IS NULL
GROUP BY c.customer_id, c.first_name, c.last_name;

API-Anfrage

---

Python

import anthropic

client = anthropic.Anthropic(
  # defaults to os.environ.get("ANTHROPIC_API_KEY")
  api_key="my_api_key",
)
message = client.messages.create(
  model="claude-opus-4-20250514",
  max_tokens=1000,
  temperature=0,
  system="Transform the following natural language requests into valid SQL queries. Assume a database with the following tables and columns exists: \n \nCustomers: \n- customer_id (INT, PRIMARY KEY) \n- first_name (VARCHAR) \n- last_name (VARCHAR) \n- email (VARCHAR) \n- phone (VARCHAR) \n- address (VARCHAR) \n- city (VARCHAR) \n- state (VARCHAR) \n- zip_code (VARCHAR) \n \nProducts: \n- product_id (INT, PRIMARY KEY) \n- product_name (VARCHAR) \n- description (TEXT) \n- category (VARCHAR) \n- price (DECIMAL) \n- stock_quantity (INT) \n \nOrders: \n- order_id (INT, PRIMARY KEY) \n- customer_id (INT, FOREIGN KEY REFERENCES Customers) \n- order_date (DATE) \n- total_amount (DECIMAL) \n- status (VARCHAR) \n \nOrder_Items: \n- order_item_id (INT, PRIMARY KEY) \n- order_id (INT, FOREIGN KEY REFERENCES Orders) \n- product_id (INT, FOREIGN KEY REFERENCES Products) \n- quantity (INT) \n- price (DECIMAL) \n \nReviews: \n- review_id (INT, PRIMARY KEY) \n- product_id (INT, FOREIGN KEY REFERENCES Products) \n- customer_id (INT, FOREIGN KEY REFERENCES Customers) \n- rating (INT) \n- comment (TEXT) \n- review_date (DATE) \n \nEmployees: \n- employee_id (INT, PRIMARY KEY) \n- first_name (VARCHAR) \n- last_name (VARCHAR) \n- email (VARCHAR) \n- phone (VARCHAR) \n- hire_date (DATE) \n- job_title (VARCHAR) \n- department (VARCHAR) \n- salary (DECIMAL) \n \nProvide the SQL query that would retrieve the data based on the natural language request.",
  messages=[
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Get the list of customers who have placed orders but have not provided any reviews, along with the total amount they have spent on orders."
        }
      ]
    }
  ]
)
print(message.content)