Using PostgreSQL as a vector database in RAG

Learn how to build a local retrieval-augmented generation (RAG) application using PostgreSQL with the pgvector extension, Ollama, and the Llama 3 large language model. This guide describes how Postgres can store both vector and tabular data, making it a versatile option for medium-sized RAG applications. It covers setting up a vector database, ingesting text from multiple sources, conducting similarity searches, and querying a large language model to generate answers. Practical coding examples and step-by-step instructions are provided to help developers get started quickly.