Implementation of Chatbot for Generating Natural Language to SQL Queries

Abstract

This paper explores the practical implementation of a Chatbot designed to connect natural language communication with database interactions by generating Structured Query Language (SQL) queries. Utilizing the OpenAPI framework, a widely-used specification for building APIs, our approach aims to enhance the flexibility and interoperability of the ChatBot. The system not only interprets user queries expressed in natural language but also transforms them into syntactically correct and semantically meaningful SQL commands. This paper provides an overview of the current landscape of natural language processing (NLP) Chatbots, discusses the architecture and design considerations of our implementation, and highlights the methodology for training and fine-tuning using state-of-the-art NLP models. Real-world use cases are presented to showcase the practical applicability of the Chatbot, demonstrating its ability to handle various complexities inherent in natural language queries. This work contributes to the field by presenting a scalable and robust solution that combines NLP and database interactions through the utilization of OpenAPI, with potential implications for improving human-computer interactions within database systems.