Everything runs locally — your OpenAI API key, your data, and your queries — so it's secure and private. Just connect your DB, describe what you want, and SnapQL writes and runs the SQL for you.
Everything runs locally — your OpenAI API key, your data, and your queries — so it's secure and private. Just connect your DB, describe what you want, and SnapQL writes and runs the SQL for you.
I wish you luck in refining your differentiation.
> I wish you luck in refining your differentiation. Can't agree more with you. It's about distribution (which Snowflake/Databricks/... have) or differentiation.
Still, chatting with your data is already working and useful for lots.
[0]: https://github.com/NickTikhonov/snap-ql/blob/409e937fa330deb...
Side note: I don't see a license anywhere, so technically it isn't open source.
For analytical purposes, this text-to-SQL is the future; it's already huge with Snowflake (https://www.snowflake.com/en/engineering-blog/cortex-analyst...).
Question, how are you testing this? Like doing it on dummy data is a bit too easy. These models, even 4o, falter when it comes to something really specific to a domain (like I work with supply chain data and other column names specific to the work that I do, that only makes sense to me and my team, but wouldn't make any sense to an LLM unless it somehow knows what those columns are)
And thank you for offering to contribute. I'll be very active on GitHub!
I could see this being incredible if it had a set of performance related queries or ran explain analyze and offered some interpreted results.
Can this be run fully locally with a local llm?
Pardon my technical ignorance, but what exactly is OpenAI's API being used for in this?
zicon35•6h ago