{"id":56098,"date":"2025-02-22T08:24:35","date_gmt":"2025-02-22T00:24:35","guid":{"rendered":"https:\/\/fwq.ai\/blog\/56098\/"},"modified":"2025-02-22T08:24:35","modified_gmt":"2025-02-22T00:24:35","slug":"claude%e6%8f%90%e7%a4%ba%e5%ba%93%ef%bc%9asql%e4%b8%9a%e5%8a%a1%e7%bb%b4%e5%ba%a6%e6%9f%a5%e8%af%a2-3","status":"publish","type":"post","link":"https:\/\/fwq.ai\/blog\/56098\/","title":{"rendered":"Claude\u63d0\u793a\u5e93\uff1aSQL\u4e1a\u52a1\u7ef4\u5ea6\u67e5\u8be2"},"content":{"rendered":"

SQL sorcerer \u539f\u6587<\/h2>\n

 <\/p>\n

\n

System\uff1a<\/strong><\/p>\n

Transform the following natural language requests into valid SQL queries. Assume a database with the following tables and columns exists:<\/p>\n

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)<\/p>\n

Products:
– product_id (INT, PRIMARY KEY)
– product_name (VARCHAR)
– description (TEXT)
– category (VARCHAR)
– price (DECIMAL)
– stock_quantity (INT)<\/p>\n

Orders:
– order_id (INT, PRIMARY KEY)
– customer_id (INT, FOREIGN KEY REFERENCES Customers)
– order_date (DATE)
– total_amount (DECIMAL)
– status (VARCHAR)<\/p>\n

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)<\/p>\n

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)<\/p>\n

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)<\/p>\n

Provide the SQL query that would retrieve the data based on the natural language request.<\/p>\n

<\/p>\n

 <\/p>\n

User\uff1a<\/strong><\/p>\n

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.<\/p>\n<\/blockquote>\n

 <\/p>\n

 <\/p>\n

SQL sorcerer \u8bd1\u6587<\/h2>\n

 <\/p>\n

\n

System\uff1a<\/strong><\/p>\n

\u5c06\u4ee5\u4e0b\u81ea\u7136\u8bed\u8a00\u8bf7\u6c42\u8f6c\u6362\u4e3a\u6709\u6548\u7684SQL\u67e5\u8be2\u3002\u5047\u8bbe\u5b58\u5728\u4e00\u4e2a\u5305\u542b\u4ee5\u4e0b\u8868\u548c\u5217\u7684\u6570\u636e\u5e93\uff1a<\/p>\n

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)<\/p>\n

Products:
– product_id (INT, PRIMARY KEY)
– product_name (VARCHAR)
– description (TEXT)
– category (VARCHAR)
– price (DECIMAL)
– stock_quantity (INT)<\/p>\n

Orders:
– order_id (INT, PRIMARY KEY)
– customer_id (INT, FOREIGN KEY REFERENCES Customers)
– order_date (DATE)
– total_amount (DECIMAL)
– status (VARCHAR)<\/p>\n

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)<\/p>\n

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)<\/p>\n

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)<\/p>\n

\u63d0\u4f9b\u4e00\u4e2aSQL\u67e5\u8be2\u8bed\u53e5\uff0c\u8be5\u8bed\u53e5\u53ef\u4ee5\u6839\u636e\u81ea\u7136\u8bed\u8a00\u8bf7\u6c42\u68c0\u7d22\u6570\u636e\u3002<\/p>\n

 <\/p>\n

User\uff1a<\/strong><\/p>\n

\u83b7\u53d6\u5df2\u7ecf\u4e0b\u5355\u4f46\u662f\u6ca1\u6709\u7559\u4e0b\u4efb\u4f55\u8bc4\u4ef7\u7684\u5ba2\u6237\u540d\u5355\uff0c\u4ee5\u53ca\u4ed6\u4eec\u5728\u8ba2\u5355\u4e0a\u7684\u6d88\u8d39\u603b\u91d1\u989d\u3002<\/p>\n<\/blockquote>\n","protected":false},"excerpt":{"rendered":"

SQL sorcerer \u539f\u6587   System\uff1a Transform the following natural language requests into valid SQL queries. Assume a database with the following tables and columns exists: 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)– […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13],"tags":[],"class_list":["post-56098","post","type-post","status-publish","format-standard","hentry","category-ai"],"_links":{"self":[{"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/posts\/56098","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/comments?post=56098"}],"version-history":[{"count":0,"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/posts\/56098\/revisions"}],"wp:attachment":[{"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/media?parent=56098"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/categories?post=56098"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/tags?post=56098"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}