Structured AI with Neo4j Predictable randomness In my very first blog on AI and Neo4j I wrote that as a developer it was amazing to take the step from just chatting with ChatGPT to actually being …

The Neo4j Blog offers insights, tutorials, and updates on graph database technology and its applications in various industries. From graph data modeling to query optimization and graph analytics, the Neo4j Blog provides resources for developers, data scientists, and business analysts looking to leverage the power of graph databases to unlock new insights from their connected data.

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Neo4j 2026.02.0 introduces a new `ai.text.structuredCompletion` Cypher procedure that returns AI-generated output conforming to a user-defined JSON schema. The post demonstrates this with a practical dinner planner app: a structured recipe is generated via OpenAI using user preferences, allergies, and recent meals, then stored as a graph with ingredient nodes, vector embeddings, and relationships. The resulting graph enables weekly dinner planning, shopping list generation, and even vector-similarity-based user matching. The post also covers the new CYPHER 25 vector search syntax and notes limitations like unit normalization in shopping lists.

Structured AI with Neo4j

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GitHub - ChristofferBergman/recipe-viewer