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Show HN: We open sourced our entire text-to-SQL product

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Re: Show HN: We open sourced our entire text-to-SQL product

#41

Post by saigal »

TheRealPomax wrote: post #33
saigal wrote: post #20

Earlier quoted context omitted.

the target audience is developers who wish to embed text to SQL functionality into their own products. the target audience is less the 'internal use case' (i.e. a data analyst) and more about letting external users do things they couldn't do before. a good example is payroll software where this type of technology can allow users to pull reports.

With what level of accuracy? And what guarantee of correctness? Because a report that happens to get the joins wrong once every 1000 reports is going to lead to fun legal problems. You still need someone who understands why you should use which approach to get the data you need without getting completely wrong numbers back that _look_ perfectly fine but reflect fantasy, not reality.

i agree that there will be "early adopter" type use cases and others that might take a while (e.g. healthcare with hipaa compliance)

it is still the early days. goal is to give the developer tools to do this easier.

Re: Show HN: We open sourced our entire text-to-SQL product

#42

Post by saigal »

winphone1974 wrote: post #40

SQL is really close to a natural language that's unambiguous, there's a few rough edges but it's not bad. Anything more natural requires a lot of context and needs to solve ambiguity.

while i agree, there is clear demand for people to use natural language to SQL. we have tremendous conviction around the desire for natural language tools, but of course the technology and product need to deliver desired results.

Re: Show HN: We open sourced our entire text-to-SQL product

#43

Post by aazo11 »

totalhack wrote: post #38

Is this more like text-to-semantic layer or does it throw the schema in the prompt and generate SQL with the llm?

This is not a text to semantic layer but it does far more than just inject schema into the prompt:

- the engine keeps an updated catalog of the data (low cardinality columns, their values etc) - taps into query history and finetunes the model to the schema - allows uploading context from unstructured sources like docs and data dictionaries - has an agent which collects all relevant info, generate the SQL, tries to retrieve a few rows to recover from errors and provides an confidence score to the generated SQL

Re: Show HN: We open sourced our entire text-to-SQL product

#44

Post by saigal »

winphone1974 wrote: post #40

SQL is really close to a natural language that's unambiguous, there's a few rough edges but it's not bad. Anything more natural requires a lot of context and needs to solve ambiguity.

"Anything more natural requires a lot of context and needs to solve ambiguity.

this is precisely why we created Dataherald-- to make it much easier to add that business context so that NL to SQL could actually be good enough to get into production

Re: Show HN: We open sourced our entire text-to-SQL product

#45

Post by aazo11 »

fpater wrote: post #15

Super cool to see this!! I've been prototyping with NL-to-SQL recently, one problem I've stumble into is how to prevent mistakes from impacting your database, be it a hallucination or even a malicious actor who was able to send a prompt to the LLM agent. I don't have much input about the questions you asked here, but feel free to contact me (info on my profile) if you'd like to talk about those other aspects!!

Sure will reach you out. Currently Dataherald blocks DML or DDL commands from being generated/executed.

Re: Show HN: We open sourced our entire text-to-SQL product

#47

Post by twojacobtwo »

saigal wrote: post #39
greenavocado wrote: post #28

Earlier quoted context omitted.

> the target audience is developers who wish to embed text to SQL functionality into their own products Who is asking?

hmm not sure I understand the question

I think GP meant 'where or from whom have you seen/heard demand for this?'.

Weirdly, I was just thinking about using an LLM to form sql queries for me, because I've forgotten much of what I knew. First time I had that thought and 5 minutes later, this fascinating idea rolls into my feed to pull me in further. I know I'm not exactly the target audience, but now I'm intrigued.

I went through a coding/design bootcamp a while back and there was virtually no focus on SQL, so a lot of my classmates were hesitant to jump into relational dbs for projects. I could see it being used in a tool for new devs or those who've focused on a JS stack and need some help with SQL.

Re: Show HN: We open sourced our entire text-to-SQL product

#49

Post by saigal »

twojacobtwo wrote: post #47
saigal wrote: post #39

Earlier quoted context omitted.

hmm not sure I understand the question

I think GP meant 'where or from whom have you seen/heard demand for this?'. Weirdly, I was just thinking about using an LLM to form sql queries for me, because I've forgotten much of what I knew. First time I had that thought and 5 minutes later, this fascinating idea rolls into my feed to pull me in further. I know I'm not exactly the target audience, but now I'm intrigued. I went through a coding/design bootcamp a…

We’ve seen demand from all types of SaaS applications where the user might need data— software that helps customer support staff answer data questions, CRM, payroll software, just to name a few.

Re: Show HN: We open sourced our entire text-to-SQL product

#50

Post by chx »

saigal wrote: post #41
TheRealPomax wrote: post #33

Earlier quoted context omitted.

With what level of accuracy? And what guarantee of correctness? Because a report that happens to get the joins wrong once every 1000 reports is going to lead to fun legal problems. You still need someone who understands why you should use which approach to get the data you need without getting completely wrong numbers back that _look_ perfectly fine but reflect fantasy, not reality.

i agree that there will be "early adopter" type use cases and others that might take a while (e.g. healthcare with hipaa compliance) it is still the early days. goal is to give the developer tools to do this easier.

Enough of this weasel talk.

It's not the early days.

Not by a country mile.

To quote Cory Doctorow

> I don’t see any path from continuous improvements to the (admittedly impressive) ”machine learning” field that leads to a general AI any more than I can see a path from continuous improvements in horse-breeding that leads to an internal combustion engine.

You can counter it doesn't necessarily need an AGI here but that doesn't change the fact you can't crank this engine harder and expect it to power an airplane.

And, as always https://hachyderm.io/@inthehands/112006855076082650

> You might be surprised to learn that I actually think LLMs have the potential to be not only fun but genuinely useful. “Show me some bullshit that would be typical in this context” can be a genuinely helpful question to have answered, in code and in natural language — for brainstorming, for seeing common conventions in an unfamiliar context, for having something crappy to react to.

> Alas, that does not remotely resemble how people are pitching this technology.

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