Navigating a Neo4j Knowledge Graph with Jev

I built neo4jev as an experiment to navigate a graph, here I explain a bit the why and how. What is Jev? And why should you care? Last week’s Typsafe.AI announcement of their Jev decision and classification model has taken the AI engineering community by storm. It is not an LLM it’s a fast, small and cheap ($42 per billion input tokens, output free) classification model that only outputs probabilities for Choice, Boolean Truth Decisions (Noul), and Scores with text and structured inputs but no text output. It doesn’t do single token at a time prediciton, but runs all predictions in parallel, so you can pass as many tasks as you want into a single API call. The model is not trained on RLHF for human alignment, but RLCD (RL for Calibrated Decisions) — so it is more like traditional ML tasks, just with a more modern model base. The docs are good, there is a Playground, and official SDKs for JavaScript and Python and inofficial ones for any other language. It has already been integrated into OpenRouter, Pydantic.ai…