LlaMA2 and Django Web Application Approach for Simplifying Table Parsing: Review

The Proposed work revolutionizes the data analytics field by implementing a Large Language Model (LLM) in a web application through which data analysis can be done easily. From the very beginning, the methodologies for data analysis are traditional methodologies such that one has to have expertise in those methodologies. But to overcome this barrier the Proposed work-based Question Answering (TabQA) application Plays a Pivotal role as it completely simplifies the data analysis methodology using the LLM model Large Language Model Meta AI (LlaMA2) by META as this model holds strong potential to analyze tabular data efficiently as it is trained on very vast data. Also, for user authentication JSON Web Token (JWT) and Google authentication are added up as a security layer. Many Businesses need these types of solutions and TabQA holds strong Potential to be scaled to enterprise-level applications. For this TabQA provides API to developers to fulfill their business needs. For fast and secure implementation Django plays an important role as a backend framework apart from that Postgre Database handles the Database management and can be deployed on the Heroku cloud for fast usage of TabQA.