a research topic for LLMs as an Interactive Database Interface for Designing Large Queries.
Text2SQL is typically considered a one-shot process where the user gives a natural language query and receives an SQL query in return. This approach is fraught with potential concerns, such as syntactical errors, logical mismatches, and schema hallucination, which often require time-consuming validations by end users. These challenges are exacerbated by the complexity of large queries typical in industry settings and the inherent ambiguity of natural language. To address these limitations, we propose a system that employs an iterative process for both query creation and validation, ensuring that the resulting data set meets the user's expectations. We tested this system against existing text-to-SQL LLM approaches using a standard industry use case, showcasing our system's ability to deliver coherent and accurate outcomes. Opportunities for future research to further refine this approach are also discussed.
[architecture](LLM chatSQL system architecture.pdf)