How a Neo4j semantic layer makes your Text-to-SQL agent smarter and cheaper

Laurent Tande discusses how using a Neo4j semantic layer can enhance the efficiency and accuracy of Text-to-SQL agents by replacing static YAML schema files with a dynamic knowledge graph approach. This shift reduces token usage by 20-30% on average and significantly increases accuracy on complex multi-table queries by approximately 10 percentage points. The Neo4j semantic layer allows agents to intelligently navigate data architecture by fetching only relevant portions of the graph, improving performance and reducing contextual noise. This method leverages database structure, constraints, and business glossaries, and incorporates user behavior from transaction logs to provide a precise context for generating SQL queries. The result is lower token usage and higher query accuracy, especially for complex questions, as the agent receives a real-time subgraph tailored to the specific query rather than a static, comprehensive schema.

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