ABSTRACT
With the aim of simplifying database interactions, this project introduces an innovative approach known as Semantic Synthesis, which transforms unstructured natural English language input into precise and contextually appropriate SQL queries. In response to the growing demand for user-friendly database access, cutting-edge Generative Artificial Intelligence (Generative AI) techniques are utilized to bridge the semantic gap between user-intended actions and the complexities of structured query language (SQL). The primary objective of this project is to empower individuals without extensive SQL expertise to interact with databases intuitively and extract knowledge from huge databases which help in making data-driven decisions, thereby enhancing usability and accessibility. To derive meaning and context from user-provided textual queries, the approach utilizes a Large Language Model (LLM) deeply integrated with transformers and a user-friendly User Interface (UI). The resulting system excels at generating SQL queries tailored to user-defined tasks and provides a detailed algorithmic breakdown to elucidate the intricacies of query development. This dual-dimensional output ensures transparency throughout the translation process and enhances comprehension of the artificial intelligence-driven conversion from natural language to SQL. This study explores the effectiveness of Semantic Synthesis across various real-world domains, including E-commerce, healthcare, education, and human resources. The project attempts to prove the system's practical utility and potential impact in diverse professional settings by assessing its accuracy, efficiency, and user-friendliness. This project aims to significantly contribute to the democratization of database interaction by adopting a comprehensive strategy that integrates technological innovation with practical usability, effectively adapting to the intricacies of different industries.
