SQLify Me: An Automated Text-To-SQL Query Generator

Abstract

Data plays a crucial role in computer science, serving as its backbone in various applications. Understanding and interacting with data stored in databases often requires technical knowledge of query languages, posing a challenge for non-Technical users. This paper aims to bridge this gap by developing a Text-To-SQL generation system, facilitating natural language queries to database operations. In SQLIFYME, we adopted a dual-path approach to implement the Text-To-SQL generation model. The Seq2Seq model, utilizing transformers, processes natural language queries to generate corresponding Structured Query Language (SQL) queries. Simultaneously, generative AI techniques are employed, applying prompt engineering to the Llama2 LLM to manage complex and nuanced queries and further refine and enhance query generation capabilities. After extensive experiments with Seq2Seq models, LLMs, and different prompt engineering techniques, we reached the optimal configuration for our proposed model. The experimental results demonstrate the effectiveness of both approaches in accurately translating natural language queries into SQL commands. The Seq2Seq model achieves high accuracy in handling structured queries, while the generative AI approach excels in handling complex and nuanced queries, highlighting the versatility of our system. Notably, the system achieved an impressive execution score of 0.8 using the T-5 base model.