Content is great, but the title is very misleading. Better: self-correcting SQL-query request generator. And the specific model is less important given the self-correcting feature, which could have been more promoted instead of jumping on the llama3 hype train. Just some food for thought
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Really awesome. Love the real business applications. This is exactly what I have been looking for.
How does de LLM know the exact table names ? How it's aware of it?
Awesome tutorials and tutorial layouts. 👍
Thank you!
can you please tell if i can do the same in vs code?
Perfect, thank you very much. Do you have a plan to chain the output to plot 😅
Nice tutorial thanks. Just as a note, the link in the description is not clickable.
Thanks! It is clickable now
@@rabbitmetrics
Thanks
what is code in .env dan gbqkey.json?
you can use the result of this chain as a dataset for fine-tuning a model.
Yes, I'm working on that. The problem is that SQL queries can be erroneous and still be executed.
great video - btw, I can't download the "schema.py" file from the written tutorial
Thanks! I've updated the written tutorial and the Colab notebook with the schema extraction functions
Content is great, but the title is very misleading. Better: self-correcting SQL-query request generator. And the specific model is less important given the self-correcting feature, which could have been more promoted instead of jumping on the llama3 hype train. Just some food for thought
Another video that shows how disappointing AI is. I can easily write those simple queries myself
a year ago it couldn't do that. Give it time and baby will grow.
you missed the entire point of this.