In my recent work on the GDSC7 challenge, I’ve been exploring how to enhance responses to subjective questions using data from the PIRLS 2021 study. By integrating the Embedchain package, I can efficiently connect to various sources like websites and videos, extract information, and store it in a vector database.

In the GDSC7 challenge, I tackled the issue of reducing API costs while preserving answer quality in agent systems. The solution involved implementing semantic search to efficiently handle gender-related queries without explicit gender columns in the database. By using embeddings for all string columns and combining questions with answers, we minimized unnecessary database queries, enhancing system efficiency and reducing costs.