In the age of data-driven decision-making, database access is crucial for both non-technical and technical users. This study presents a powerful database chatbot that facilitates interaction with databases in human language and which does not require advanced SQL knowledge. It features chatbot-specific processes like handling user input, interpreting natural language, and presenting SQL queries; under the hood, this layer operates via Lang chain, where we leverage advanced language models. The chatbot can respond in a range of languages, catering to users who speak different languages.

Its multilingual support is one of the key reasons why this chatbot stands out, as it encourages users to engage in both regional and international languages, thereby including the entire spectrum of the population. Other important features are the inclusion of Google Text-to-Speech (GTTS), which makes this software text-to-speech- accessible, especially for users who have disabilities and want audio output. The app also allows users to copy responses to the clipboard and download all responses for greater flexibility and convenience.

Another advantage is session persistence. The chatbot can store session information so that it remembers the context of the conversation (i.e., I have been chatting with you and keep track of my previous messages, etc.). This is also powered by SQL and database semanticqueries, as well as context-aware responses to give better solutions. Future work will require further database compatibility, more query optimization, and advanced contextual conversation management to provide an even richer user experience.

As it bridges the gap between users and database systems using natural language processing, this project simplifies the way a database is handled by everyone over the globe, thereby making access to data easier through enhancing its usability for every kind of audience.