# OpenAI DevDay 2024 | Community Spotlight | Supabase

## Метаданные

- **Канал:** OpenAI
- **YouTube:** https://www.youtube.com/watch?v=0_sHinA0RV0
- **Дата:** 17.12.2024
- **Длительность:** 9:36
- **Просмотры:** 3,116
- **Источник:** https://ekstraktznaniy.ru/video/11428

## Описание

database.build - the AI powered Postgres sandbox

## Транскрипт

### Segment 1 (00:00 - 05:00) []

thank you hi I'm Thor I work on developer experience at superbase now before we jump in can I get a quick show of fans uh who your create software for a living okay a bunch of folks and uh keep your hand up if you really enjoy writing database migrations yeah I thought so um and that's exactly why we've created database. built the AI powered postgress playground in the browser now you can think of it like jet gpt's code interpreter but for postgress giving an AI model full autonomy over the database means that it can run multiple operations backto back without delay it makes AI feel even more humanlike and useful this novel experience is only part possible thanks to gp4 O's incredible understanding of postgress and SQL paired with a disposable in browser database so we don't have to worry about data loss and we can just let the model run wild so if you want to play along you can just go to database. build and um let's do some life demo here uh I did some offerings to the demo Gods earlier so let's track some movies so we can just fire that off and we can see now we're going to GPT 40 uh we're getting some SQL we're running that SQL and the SQL that we're getting back from the model we're running it directly on the database that is running in the browser so this is using PG light um which is running postgress in the browser through WM uh and then we can see here okay the movies table has been created fantastic so that's it we got our first migration uh in the box now how are we able to give the model so much autonomy and really the secret Sauer is tool calling so we use tool calls not only to execute SQL in PG light but also to perform other actions that you would normally only find kind of in the graphical user interface okay let's dive a bit deeper under the hood to see kind of what's Happening uh we're using the versel AI SDK here and it's been a great open source tool to iterate quickly uh and actually this is the new uh r. o open AI theme uh I think it was released yesterday so uh big shout out to Edwin and the ray team for making that uh open so you can get some really nice code screenshots there and of course like the rest of super Bays this is open source as well so you can jump in and have a look uh later yourself but so this is where the magic happens so on tool call provides the client side tools that the model can automatically invoke and Max steps defines a limit in case we get kind of stuck in an infinite Loop so let's take the example of the execute SQL tool call here um we set up our tool call schema kind of using sort uh in typescript and then we have a use on tool called hook which implements the tool functionality so we sanitize the response a bit and then return the query results uh and the updated schema here so let's step through our demo earlier where we kind of said we want to track some movies so initially we make an artificial tool call uh to get the database schema and share that as context with um the model together with kind of the users's message here uh which just says track some movies um and so in the response we get the execute SQL tool call uh together with our create table SQL which we then um immediately kind of run on the database so we're running that query client site in the in browser database and then we feed the query results and the updated schema back to the model to generate kind of the streaming response that we saw here where it says the movies table has been created and we render that to the I and lastly um in a final step we asked the model to rename the conversation and with that our database uh and so we see here the conversation was renamed to movie tracking database and that's our final tool call here and these are just happening back to back with kind of any user interaction so as you can see with kind of all the functionality being tool calls that means the model has a lot of autonomy to chain various tools

### Segment 2 (05:00 - 09:00) [5:00]

together uh and this also enables the application to self-heal uh for example any SQL errors from postest are fed back to the language model so that it can try a few more attempts at kind of solving um the issue another really cool feature uh here is the inbuilt um in uh the built-in Vector embedding support using PG vector and Transformers Js so let's go back to our demo and actually say add five movies um and embeddings on their titles and so ah I probably should be typing add five movie examples and embeddings on their titles so we're going to fire this off and we're going to run again kind of our multi-step um models here hopefully come on no okay I think we might have ran into an issue here so what it should do is let me quickly reload that if we can recover okay I get my demo got offerings weren't accepted let's try it again see okay we're working on it and so now you can see we're first um executing our SQL to kind of seed some sample data in our database and then in the next step we're generating the embeddings for all the titles and we're storing them in a separate database because if you worked with Vector embeddings before even with small models and you know smaller Dimensions these are pretty large so we kind of want to keep them outside and you can see okay we got kind of five movies in Here and Now what we can do is by having the embeddings um in the database as well we can do semantic search right so we can say use semantic search to find a movie about Batman okay and so now we're going to go off again we're running a couple steps back to back uh so we're going to generate embeddings you know for Batman and then what we're doing here is so we're using PG Vector um to perform cosine distance to find kind of all the movies that are related to Batman and then we get back okay the dark KN fantastic so we have um you know semantic search built into this tool a really neat really neat feature here now just like copy and te make for great drink so does mixing a traditional graphical user interface with a large language model so all of the actions in the UI are implemented as tool calls and when an action is clicked in the user interface we simply fire off a message in the chat and let the model handle the rest now a little warning while this is great for your user experience and iterating quickly it's not so great for your wallet so do watch out for that another great feature here charts so we're using chartjs here uh one of the more mature charting libraries available in JavaScript and the reason we chose it is because GPT 40 has a really good understanding ing of the syntax and configuration so by simply chatting to the model you can fully customize kind of anything that you can do with chartjs you can change um the chart type the AES the colors uh so as long as chartjs supports it uh GPD 40 can make it happen lastly quick look at impact and next steps here so we were able to sign up more than 60,000 users in 3 months so I think there's definitely something here um we've also just launched live share which allows you to connect to your in browser database from any postgress client um we just published a blog post about this the tech um is really fantastic so do check that out um a bunch more interesting stuff happening but that's pretty much all I have time for so if you have any questions just find me later but the chist I want you to take away is that tool calls plus full database access make for a pretty powerful postest sandbox thank you