The missing pieces to your AI app (pgvector + RAG in prod)

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Workshop GitHub repo:

It's easy to build an AI proof-of-concept (POC), but how do you turn that into a real production-ready application?

What are the best practices when implementing:

- Retrieval augmented generation (RAG)
- Authorization (row level security)
- Embedding generation (open source models)
- pgvector indexes
- Similarity calculations
- REST APIs
- File storage

00:00 Intro
01:06 Demo & setup
05:28 Step 1 (File storage)
31:40 Step 2 (Documents & splitting)
1:19:02 Step 3 (Embeddings)
1:36:32 Step 4 (Chat & RAG)
2:10:11 Demo & next steps
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This is truly an unbelievable tutorial. College professors could learn a lot from you. The fact that this quality is free is mind blowing

Mburdo
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This video is mindblowing. 10/10
I'm a Tech Lead, with more than 15years in software engineering and I can already feel that this video is a game changer for me.
So much crucial/game changing infos gathered in an incredible way, with amazing presentation and pace.

VictorSouto
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I had this in my todo, it's mind blowing. Well detailed, great speed, tis is amazing. My only regret is not have watched it before. Thank you so much

sgruhier
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I'm sold! Diving deeper into Supabase because of this :) Great 2 hours content!

CarloL
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Give this man a raise. Good speed and complete. 9.99/10 - nobody gets a 10

shinchima
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this is one of the best video came out of Supabase. Please do more such detailed videos. thanks

dheerajs
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Please do more videos like this, truly amazing work. This helps me prevent a whole lot of headaches. I love Supabase 💚

sumodd
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One of the best tutorials I've seen in my entire life. Everything is clear, no shortcuts, no analogies, no abtractions. we learn a lot of things from different fields along with production best practices.
This is how tutorials should be.
Thank you so much ! Looking forward to learn more from you

brucewayne
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Thank you for the great video!! Would like to see more videos on implementing Supabase using Python (not sure about the demand actually) if possible. :)

ferrychandra
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This an amazing guide. Like absolutely amazing, bravo.

benjaminw
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Joining the crowd - this is one of the best tutorials I have ever seen (and I have seen many). Great Job!

JohnBucy-rzqo
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It's brilliant. Just keep me breath outhht between important pieces of code! …next time!
Dynamic of video is really good – but pieces where I need to learn something new, wish to look for references, sources… I cannot ever hit space… tracking back 🙂
BTW Thank you for so great tutorial! 🙂

PavelLang
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worst ever tutorial. so unclear, it cannot be more confusing

MaksymLiamin-yn
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This is one amazing video. Thanks so much!
One suggestion, will be super cool to have aversion if this video using langchain as well.
There are a lot of great benefits using it instead of going directly to Open AI (like the ability to easily switch or use multiple model providers)

shaharnechmad
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Fantastic in depth walkthrough with code examples and reasoning behind implementation decision. Helped me understand supabase, its services and architecture and how things fit together much more. Thank you!

mtin
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There were so many parts to like in this video, my favourite was how to extract the authorisation headers in making the call to a REST endpoint. Will probably implement the endpoint in python with Fast API rather than Deno. 😂

rembautimes
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This is great. What changes would need to be made to use this with an open source model like Mistral or Llama 2? Is it just whatever model library is used and the embedding model that goes with it?

jonathanma
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Superbase. I freaking love you. Long live the king

MachineLearningZuu
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Man you are consistently saving me when I hit a wall on my projects. Thank

MrArbitraryNumber
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Great stuff, thanks a lot!
One question. What's the point of deploying Deno edge functions (and calling them with pg_net inside postgres) instead of simply using Next.js actions for processing files after upload? It adds a lot of complexity imo. Any real benefits?

nikitastriuk