Show HN: Describe SQL using natural language, and execute against real data

https://blog.seekwell.io/gpt3

Would be happy to chat and compare notes if you'd like!

I thought it was funny in that in the weather dataset that "NULL" comes in first for the win for some questions:

> what is the all time rainiest city? > what are the top 5 most dry states?

The query conversion is impressive!

At Veezoo (http://www.veezoo.com) we have been tackling this problem for over 5 years now.

Under the hood we are using our own models. With GPT-3 we're a bit worried about the lack of fine-grained control needed for productive use-cases and obv. also lock in.

Will try out against the same dataset and see how it compares!

ie, "ERROR: Syntax error: Expected end of input but got keyword END at [49:3]"

How can I extend this to use other datasets? There seem to be quite a few interesting publicly available datasets out there: https://console.cloud.google.com/marketplace/browse?filter=s...

and I'm wondering if: 1. Is there a paid-for version of your app/website where I can plug in a diff dataset? 2. Have you considered sharing the source code for others to recreate and plug in diff datasets? 3. Or, :sweat_smile:, perhaps this is as simple as adding new datasets to the drop-down menu? say, finance data / Bitcoin transactions data?

Regardless, really cool app!

As an interesting test case, check out the very strange and seemingly recursive query generated for “Get the top 10 authors of caching libraries, ranked by commit volume”

If we could get even 50% success rate on a reasonable starting point for the generated SQL each time, that would be the biggest value-add our organization has ever seen.

I think our use case is compelling because we have to implement the same SQL targets for every customer. The only variations are typically customer-specific parameters/codes/etc.

We also have a huge corpus of examples to pull from for training data.

We are thinking about initially implementing some higher order views/functions in our SQL dialect to make things easier on ourselves with the GPT model. Complex joins across many tables seems to be something that would still elude these techniques. Most of our joins are of a very particular shape, so we can abstract the super nasty stuff away.

Worst case scenario, this concludes like my cynical mind assumes it will, but I am open to being surprised this time. We aren't going to put everything behind this, more of a "if it works..." kind of 1-2 week experiment.

   /*
   Describe the data in the all_geoclustered table.
   */

   SELECT
     COUNT(*) AS total_rows,
     COUNT(*) FILTER (WHERE date IS NOT NULL) AS total_rows_with_date,
     COUNT(*) FILTER (WHERE wban IS NOT NULL) AS total_rows_with_wban,
     COUNT(*) FILTER (WHERE stn IS NOT NULL) AS total_rows_with_stn,
     COUNT(*) FILTER (WHERE year IS NOT NULL) AS total_rows_with_year,
     COUNT(*) FILTER (WHERE mo IS NOT NULL) AS total_rows_with_mo,
     COUNT(*) FILTER (WHERE da IS NOT NULL) AS total_rows_with_da,
     COUNT(*) FILTER (WHERE temp IS NOT NULL) AS total_rows_with_temp,
     COUNT(*) FILTER (WHERE count_temp IS NOT NULL) AS total_rows_with_count_temp,
     COUNT(*) FILTER (WHERE dewp IS NOT NULL) AS total_rows_with_dewp,
     COUNT(*) FILTER (WHERE count_dewp IS NOT NULL) AS total_rows_with_count_dewp,
     COUNT(*) FILTER (WHERE slp IS NOT NULL) AS total_rows_with_slp,
     COUNT(*) FILTER (WHERE count_slp IS NOT NULL) AS total_rows_with_count_slp,
     COUNT(*) FILTER (WHERE stp IS NOT NULL) AS total_rows_with_stp,
     COUNT(*) FILTER (WHERE count_stp IS NOT NULL) AS total_rows_with_count_stp,
     COUNT(*) FILTER (WHERE visib IS NOT NULL) AS total_rows_with_visib,
     COUNT(*) FILTER (WHERE count_visib IS NOT NULL) AS total_rows_with_count_visib,
     COUNT(*) FILTER (WHERE wdsp IS NOT NULL) AS total_rows_with_wdsp,
     COUNT(*) FILTER (WHERE count_wdsp IS NOT NULL) AS total_rows_with_count_wdsp,
     COUNT(*) FILTER (WHERE mxpsd IS NOT NULL
     
     
   /*
   How often do hail and thunder happen together?
   */

   SELECT
     COUNT(*) AS count,
     SUM(IF(hail=1 AND thunder=1, 1, 0)) AS sum_hail_thunder
   FROM
     `fh-bigquery.weather_gsod.all`
   WHERE
     year = 2019
     AND mo = 11
     AND da = 14
     AND stn = '725030'

   /*
   What time of day does hail usually happen?
   */

   SELECT
     date,
     wban,
     stn,
     year,
     mo,
     da,
     temp,
     count_temp,
     dewp,
     count_dewp,
     slp,
     count_slp,
     stp,
     count_stp,
     visib,
     count_visib,
     wdsp,
     count_wdsp,
     mxpsd,
     gust,
     max,
     flag_max,
     min,
     flag_min,
     prcp,
     flag_prcp,
     sndp,
     fog,
     rain_drizzle,
     snow_ice_pellets,
     hail,
     thunder,
     tornado_funnel_cloud,
     usaf,
     name,
     country,
     state,
     call,
     lat,
     lon,
     elev,
     begin,
     end,
     point_gis,
     fake_date
   FROM
     `fh-bigquery.weather_gsod.all_geoclustered`
   WHERE
     hail = 1
   ORDER BY
     date DESC
   LIMIT
     10

   /*
   What is the range of elevations where tornadoes happen?
   */

   SELECT
     elev,
     COUNT(*) AS count
   FROM
     `fh-bigquery.weather_gsod.all`
   WHERE
     tornado_funnel_cloud = 'T'
   GROUP BY
     elev
   ORDER BY
     count DESC

SELECT date, wban, stn, year, mo, da, temp, count_temp, dewp, count_dewp, slp, count_slp, stp, count_stp, visib, count_visib, wdsp, count_wdsp, mxpsd, gust, max, flag_max, min, flag_min, prcp, flag_prcp, sndp, fog, rain_drizzle, snow_ice_pellets, hail, thunder, tornado_funnel_cloud, usaf, name, country, state, call, lat, lon, elev, begin, end, point_gis, fake_date FROM `fh-bigquery.weather_gsod.all_geoclustered` WHERE lat IS NOT NULL AND lon IS NOT NULL AND lat != 0 AND lon != 0 AND lat != 1 AND lon != 1 AND lat != -1 AND lon != -1 AND lat != 2 AND lon != 2 AND lat != -2 AND lon != -2 AND lat != 3 AND lon != 3 AND lat != -3 AND lon != -3 AND lat != 4 AND lon != 4 AND lat != -4 AND lon != -4 AND lat != 5 AND lon != 5 AND lat != -5 AND lon != -5 AND lat != 6 AND lon != 6 AND lat != -6 AND lon != -6 AND lat != 7 AND lon != 7 AND lat != -7 AND lon != -7 AND lat != 8 AND lon != 8 AND lat != -8 AND lon