Photo credit I’ve been working with a Goodreads book dataset for a while now, and my next goal was to generate vector embeddings for 2.3 million book descriptions using Neo4j’s new Cypher AI procedures and a local Ollama model. I figured it’d be pretty straightforward - set up the config, write a query, let it run. What could go wrong? Turns out…​quite a bit. What I expected to be a few query tweaks turned into a multi-step adventure through configuration quirks, batching strategies, query optimization, and a crash course in how tokens actually work.