[MrezaPourreza]

Thank you for your interest in our work. The schema linking approach employed in our agent significantly differs from the one described in my paper. In the paper, we utilized a method that involved breaking down questions and matching entities with the table schema. However, when dealing with large datasets, the approach outlined in the DIN-SQL paper is no longer feasible, as we cannot feasibly provide all table names and columns within the prompt. As a result, we opted for an alternative method, leveraging table embeddings along with the NL question embedding to facilitate the schema linking process.

Regarding the context store you mentioned, it plays a vital role in our system. We use the context store to retrieve previously verified questions from earlier interactions. This functionality assists the agent in locating the correct tables and columns based on the knowledge gained from past interactions with our system.