SQL Evaluation via Semantic Analysis Without Target Queries
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Solution Overview
Problem
Existing SQL evaluation methods struggle with accurately assessing the correctness and semantic accuracy of generated SQL statements, particularly in complex business scenarios where standard SQL references are lacking, leading to inefficiencies and potential misjudgments.
Innovation Solution
A structured query language evaluation method using a large language model to generate predicted SQL, with dual evaluation paths: comparing against a target SQL when available and employing semantic analysis when not, ensuring accurate evaluation through syntax and execution result comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SQL evaluation is performed by comparing with target SQL, then evaluation accuracy is improved, but the method becomes inapplicable when target SQL is unavailable
Solution Approach 1:
The evaluation method dynamically adapts between two modes: when target SQL is available, it uses comparison-based evaluation for high accuracy; when target SQL is unavailable, it switches to semantic analysis-based evaluation for broader applicability. This dynamic switching resolves the contradiction between accuracy and adaptability.
Solution Approach 2:
The patent introduces semantic analysis as an intermediary evaluation approach that bridges the gap when direct SQL comparison is not possible. The semantic analysis module analyzes the logical meaning and structure of generated SQL without requiring a target SQL for comparison, enabling evaluation in scenarios where target SQL is unavailable.
2Measurement precision
If developers manually evaluate SQL correctness, then evaluation thoroughness is improved, but development efficiency deteriorates
Solution Approach 1:
The system enables self-service evaluation where the SQL evaluation module automatically assesses generated SQL statements without requiring manual developer intervention. The system uses large language models to perform semantic analysis and evaluate SQL correctness autonomously, maintaining thoroughness while significantly improving development efficiency.
Solution Approach 2:
The patent replaces the mechanical manual evaluation process with an automated computational system using large language models. The AI-based evaluation module performs semantic analysis and correctness assessment that previously required human developers, thereby maintaining evaluation quality while freeing developers for higher-value tasks.
3Manufacturing precision
If SQL generation relies on developer expertise, then SQL quality is improved, but the complexity of operation increases
Solution Approach 1:
The patent introduces a large language model as an intermediary between natural language queries and SQL generation. The model translates user intent into correct SQL statements, eliminating the need for developers to manually write complex SQL while maintaining high quality through semantic understanding and generation capabilities.
Solution Approach 2:
The system enables self-service SQL generation where the large language model autonomously generates correct SQL statements based on user requirements without needing developer expertise. The model handles the complexity of SQL construction internally, allowing users with basic knowledge to generate high-quality SQL effortlessly.
Data Source
AI summary
Provided is a structured query language evaluation method, an electronic device and a storage medium, relating to a technical field of data processing, and specifically to technical fields of large model and natural language processing. The method includes: obtaining a query question; generating a predicted structured query language based on the query question by using a large language model; in presence of a target structured query language, evaluating accuracy of the predicted structured query language based on the predicted structured query language and the target structured query language to obtain an evaluation result corresponding to the predicted structured query language; and in absence of the target structured query language, evaluating the accuracy of the predicted structured query language based on a semantic analysis result of the predicted structured query language to obtain the evaluation result corresponding to the predicted structured query language.