hi guys i have a question on one of the latest video/documentation from llama_index on LLMs for Advanced Question-Answering over Tabular/CSV/SQL Data (Building Advanced RAG, Part 2)
Jerry Liu is indexing all his files first (he got tons of CSV files) but i was trying to do the same on my SQL, one of my table only has 27,000 rows (the other one is like 1 milions rows) and even the small table it tooks ages to index. i know his doing that to then use get_table_context_and_rows_str to give the AI some relevant rows
how can i do it ? is it because im supposed to save my SQL table data into CSv? is that why its taking so long ?
well, i just saw your reply on youtube, but i was wondering in the video of Jerry, it was fast, was it becasue each of his "table" was very small ?
i dont use CSV, i basicall do select * from {mytable} but the table is 27,000 rows thats the smallest table, the JSON file went up to 800mb, imagine the table of sales of 1 million rows, it does not make any sense
but i do love what hes doing but i wonder how i can make it happen (he was solving the problem to give some rows context for the "B.I.G" dot problem"
