RAGtime with Postgres: AI Power with pgvector and Retrieval-Augmented Generation

📚 PostgreSQL Mistakes and How to Avoid Them: https://mng.bz/vKd4 Follow me on: 🦣 https://fosstodon.org/@vyruss 🦋 https://bsky.app/profile/vyruss.org   / vyruss   Talk at FOSSY 2025 Retrieval-Augmented Generation (RAG) is a powerful paradigm in application development with AI. In this talk, we'll demonstrate how to leverage PostgreSQL with pgvector to combine the strengths of vector similarity search with Large Language Models (LLMs). As the speaker is a Postgres nerd (not an AI expert), we'll explain in simple terms how to dip your toes into AI while leveraging our favorite database -- from the perspective of a database person learning to work with these new tools. We'll walk through: How to use pgvector to store and search vector embeddings (and what those are) How to connect these capabilities with AI LLMs to build intelligent applications. Some practical tips for implementation, including configuration, indexing strategies, and scaling considerations How to reduce dependency on expensive external AI services by using open-source models while maintaining control over costs and infrastructure To demonstrate these concepts in action, we'll look at a real-world example of building a developer assistance system that helps teams understand their codebase.