AI/ML

RAG Series – Naive RAG

Introduction Since my last series on pgvector I had quite a fun time to work on RAG workflows on pgvector and learned some valuable lessons and decided to share some of it in a blog post series on the(…)

Technological debt with AI workflows: When data hits the fan?

AI projects are increasing and with it comes some risks that are or not related to this technology. Some organizations are jumping straight into “plug the LLM into a vector database” ( like Pinecone, Qdrant, pgvector…) at the(…)

pgvector, a guide for DBA – Part3 AI agent and workflows

Introduction Nowadays, everyone in IT is well versed in using ChatGPT and other LLM-powered Chat to retrieve information like Google or try to learn something with it. The problem with those models is their accuracy. Current AI models only(…)

pgvector, a guide for DBA – Part1: LAB DEMO

Introduction The first release of pgvector occurred in April 2021. Since then a lot has changed and is still changing. Even though we could say that the level of maturity of AI stacks and usage is currently really amazing,(…)