Building a RAG application with a PostgreSQL database

Learn how to build a Retrieval Augmented Generation (RAG) application using PostgreSQL from scratch. RAG leverages large language models to answer questions accurately by providing relevant information from databases. The post walks through setting up PostgreSQL with the PG Vector extension, creating embeddings for semantic search, and combining full-text and vector search techniques for optimal results. It also highlights the importance of query rewriting and discusses options for using local and hosted models for embedding.