Lilly Tech Systems
Supabase + AI
Combine Supabase's open-source backend with AI capabilities. Learn to store and query vector embeddings with pgvector, build RAG pipelines, create similarity search, and deploy AI-powered Edge Functions — all on top of Postgres.
What You'll Learn
By the end of this course, you'll be able to build AI-powered backends with Supabase, pgvector, and Edge Functions.
pgvector & Embeddings
Store and query high-dimensional vector embeddings directly in Postgres using the pgvector extension.
Similarity Search
Build semantic search using cosine similarity, inner product, and L2 distance functions on vector columns.
RAG Pipelines
Implement Retrieval-Augmented Generation by combining vector search results with LLM prompts for grounded answers.
Edge Functions
Deploy serverless AI functions on Supabase Edge Functions with Deno, running close to your users worldwide.
Course Lessons
Follow the lessons in order or jump to any topic you need.
1. Introduction
Why Supabase for AI? Explore how Postgres, pgvector, and Edge Functions create a powerful AI backend platform.
2. pgvector Setup
Enable pgvector in Supabase, create vector columns, configure indexes, and understand embedding dimensions.
3. Embeddings
Generate embeddings with OpenAI, Cohere, or open-source models and store them in Supabase for later retrieval.
4. Vector Search
Build semantic search with RPC functions, filtered queries, hybrid search, and result ranking strategies.
5. Edge Functions
Deploy AI-powered Edge Functions for embedding generation, RAG queries, and real-time AI with Supabase Realtime.
6. Best Practices
Production patterns for indexing strategies, cost management, scaling vectors, security, and monitoring AI workloads.
Prerequisites
What you need before starting this course.
- Basic knowledge of SQL and relational databases
- A Supabase account (free tier works)
- An API key from OpenAI or another embedding provider
- Familiarity with JavaScript/TypeScript