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I tried using the following prompt:

write up a data quality check rules demo for the Instacart Market Basket Analysis dataset from Kaggle. Do not write any code. This is a theoretical look. give detailed explanations for everything. I want a demo specific to this dataset, not a general explanation of dqc. The schema for the dataset is described as follows: The Kaggle Instacart Market Basket Analysis Dataset comprises several key tables. The "Orders" table features columns like "order_id," a unique identifier for orders, and "user_id," a corresponding user identifier. It also includes "eval_set," specifying the dataset split, and "order_number" denoting the order sequence. "Order_dow" signifies the day of the week, while "order_hour_of_day" indicates the hour of order placement. "Days_since_prior_order" reflects the duration since the previous order. The "Products" table contains "product_id," a unique product identifier, and "product_name," describing the product. "Aisle_id" and "department_id" link products to their respective aisles and departments. The "Order Products" table holds "order_id" and "product_id" as foreign keys, detailing the order sequence via "add_to_cart_order" and reorders with a "reordered" flag. Lastly, the "Aisles" table specifies "aisle_id" and "aisle," while the "Departments" table encompasses "department_id" and "department" names. These tables constitute the dataset's schema, facilitating comprehensive analyses of grocery shopping patterns and user preferences.

This is the error I recieved: TypeError: '>' not supported between instances of 'float' and 'str'

Could you please provide more details?

1. Detailed steps to reproduce the issue.
2. Any error messages or logs you've encountered.

your prompt is too long and have double quotes, if you want to try it, please replace double quotes with quotes,

python startup.py "write up a data quality check rules demo for the Instacart Market Basket Analysis dataset from Kaggle. Do not write any code. This is a theoretical look. give detailed explanations for everything. I want a demo specific to this dataset, not a general explanation of dqc. The schema for the dataset is described as follows: The Kaggle Instacart Market Basket Analysis Dataset comprises several key tables. The 'Orders' table features columns like 'order_id,' a unique identifier for orders, and 'user_id,' a corresponding user identifier. It also includes 'eval_set,' specifying the dataset split, and 'order_number' denoting the order sequence. 'Order_dow' signifies the day of the week, while 'order_hour_of_day' indicates the hour of order placement. 'Days_since_prior_order' reflects the duration since the previous order. The 'Products' table contains 'product_id,' a unique product identifier, and 'product_name,' describing the product. 'Aisle_id' and 'department_id' link products to their respective aisles and departments. The 'Order Products' table holds 'order_id' and 'product_id' as foreign keys, detailing the order sequence via 'add_to_cart_order' and reorders with a 'reordered' flag. Lastly, the 'Aisles' table specifies 'aisle_id' and 'aisle,' while the 'Departments' table encompasses 'department_id' and 'department' names. These tables constitute the dataset's schema, facilitating comprehensive analyses of grocery shopping patterns and user preferences."

it works.

of course, the output may not what you want.