LLM to check the equivalence of a SQL query against a reference SQL query using the sql prompt.from langchain.evaluation.qa.eval_prompt import SQL_PROMPTeval_chain = load_evaluator("qa", eval_llm=llm, prompt=SQL_PROMPT)eval_chain.evaluate_strings(    input="What's last quarter's sales numbers?",    prediction="""SELECT SUM(sale_amount) AS last_quarter_salesFROM salesWHERE sale_date >= DATEADD(quarter, -1, GETDATE()) AND sale_date < GETDATE();""",    reference="""SELECT SUM(sub.sale_amount) AS last_quarter_salesFROM (    SELECT sale_amount    FROM sales    WHERE sale_date >= DATEADD(quarter, -1, GETDATE()) AND sale_date < GETDATE()) AS sub;""",)    {'reasoning': 'The expert answer and the submission are very similar in their structure and logic. Both queries are trying to calculate the sum of sales amounts for the last quarter. They both use the SUM function to add up the sale_amount from the sales table. They also both use the same WHERE clause to filter the sales data to only include sales from the last quarter. The WHERE clause uses the DATEADD function to subtract 1 quarter from the current date (GETDATE()) and only includes sales where the sale_date is greater than or equal to this date and less than the current date.\n\nThe main difference between the two queries is that the expert answer uses a subquery to first select the sale_amount from the sales table with the appropriate date filter, and then sums these amounts in the outer query. The submission, on the other hand, does not use a subquery and instead sums the sale_amount directly in the main query with the same date filter.\n\nHowever, this difference does not affect the result of the query. Both queries will return the same result, which is the sum of the