Navigating a Neo4j Knowledge Graph with Jev

Neo4j’s Jev experiment, neo4jev, explores using TypeSafe AI’s Jev decision model to navigate knowledge graphs by selecting among actual graph relationships rather than generating text or queries. Jev is presented as a fast, low-cost probabilistic classification system that returns choices, truth-like confidence scores, and numeric scores in parallel, making it suited to repeated software decisions that would be slow or unreliable with conventional LLMs. The prototype performs beam-search traversal of Neo4j’s public Companies graph, evaluating outgoing and incoming edges at each node, checking whether a destination or goal has been reached, and recording probability distributions for every decision. It supports navigation toward a specified node, a path-shaped intent, or a free-text objective, while limiting cycles, search depth, API calls, and the number of candidate edges. The author reports that performance depends heavily on providing clean, concise graph data, schema context, balanced candidate sampling across relationship types, and meaningful goal definitions, with vague free-text goal checks remaining a limitation. The project demonstrates a broader approach in which AI systems make transparent, grounded choices from fetched options for applications such as query planning, workflow routing, tool selection, and other probabilistic decision tasks.

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