SUPABASE

A step-by-step guide to going from pgvector to prod using Supabase. We'll discuss best practices across the board so that you can be confident deploying your application in the real world. Learn more about pgvector: https://supabase.com/docs/guides/database/extensions/pgvector?utm_source=yutube&utm_medium=social&utm_campaign=pgvector&utm_content=ibzlEQmgPPY Workshop GitHub repo: https://github.com/supabase-community/chatgpt-your-files 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