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Show HN: SnapQL – Desktop app to query Postgres with AI

(github.com)
103 points nicktikhonov | 2 comments | 20 Jun 25 11:08 UTC | HN request time: 0.435s | source

SnapQL is an open-source desktop app (built with Electron) that lets you query your Postgres database using natural language. It’s schema-aware, so you don’t need to copy-paste your schema or write complex SQL by hand.

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.

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joshstrange ◴[21 Jun 25 11:46 UTC] No.44336777[source]▶
I might test this out, but I worry that it suffers from the same problems that I ran into the last time I played with LLMs writing queries. Specifically not understanding your schema. It might understand relations but most production tables have oddly named columns, potentially columns that changed function overtime, potentially deprecated columns, internal-lingo columns, and the list goes on.

Granted, I was using 3.5 at the time, but even with heavy prompting and trying to explain what certain tables/columns are used for, feeding it the schema, and feeding it sample rows, more often than not it produced garbage. Maybe 4o/o3/Claude4/etc can do better now, but I’m still skeptical.

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1. nicktikhonov ◴[21 Jun 25 12:05 UTC] No.44336911[source]▶
might be possible to solve this with prompt configuration. e.g. you'd be able to explain to the llm all the weird naming conventions and unintuitive mappings
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2. joshstrange ◴[21 Jun 25 12:13 UTC] No.44336972[source]▶
I did that the last time (again, only with 3.5, things have hopefully improved in this area).

And I could potentially see LLMs being useful to generate the “bones” of a query for me but I’d never expose it to end-users (which was what I was playing with). So instead of letting my users do something like “What were my sales for last month?” I could use LLMs to help build queries that were hardcoded for various reports.

The problem is that I know SQL, I’m pretty good at, and I have a perfect understanding of my company’s schema. I might ask an LLM a generic SQL question but trying to feed it my schema just leads to (or rather “led to” in my trials before) prompt hell. I spent hours tweaking the prompts, feeding it more context, begging with it to ignore the “cash” column that has been depreciated for 4+ years, etc. After all of that it still would make simple mistakes that I hard specially warned against.