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 matter. We will discover together where all RAG best practices are landing for the past 2 years and how can as…
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 risk of replaying the lift-and-shift mistakes made on the cloud projects. This time, hiding years of data-quality, governance, code, architectural debt behind…
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 know what information they have been fed and expose it to you in a format you understand (which is already surprisingly…
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, things are still changing fast and will probably continue to do so for a while. This is quite exciting because as…
