Using Supabase’s vector database with PostgreSQL

Modern technology's integration of artificial intelligence (AI) and machine learning (ML) enhances application contextual awareness, with vectors and embeddings being key components in processing complex data. The article explores the application of these concepts using Supabase, an open-source alternative to Firebase, to manage vector data in PostgreSQL databases. It highlights creating embeddings with OpenAI to transform data into numerical forms, enabling functionalities like search, clustering, recommendations, and anomaly detection. The guide includes practical steps for enabling vectors in Supabase, creating embeddings, and implementing search functionality through a PostgreSQL function. By utilizing Supabase's pgvector extension and OpenAI’s API, developers can create intelligent, responsive applications with enhanced data interaction capabilities.