This article details how to automate embedding generation and updates in Postgres using Supabase Vector, Queues, Cron, and pg_net extension with Edge Functions, addressing the issues of drift, latency, and complexity found in traditional external embedding pipelines.
This article explores how to implement a retriever over a knowledge graph containing structured information to power RAG (Retrieval-Augmented Generation) applications.
pgai brings AI workflows to your PostgreSQL database. It simplifies the process of building search and Retrieval Augmented Generation (RAG) AI applications with PostgreSQL by bringing embedding and generation AI models closer to the database.
