Databases are the most critical assets for enterprises, and yet they remain largely inaccessible to people who make the most important decisions. In this paper, we describe the Tursio search platform that builds an abstraction layer, aka semantic knowledge graph, over the underlying databases to make them searchable in natural language. Tursio infuses large language models (LLMs) into every part of the query processing stack, including data modeling, query compilation, query planning, and result reasoning. This allows Tursio to process natural language queries systematically using techniques from traditional query planning and rewriting, rather than black-box memorization. We describe the architecture of Tursio in detail and present a comprehensive evaluation on production workloads, and synthetic and realistic benchmarks. Our results show that Tursio achieves high accuracy while being efficient and scalable, making databases truly searchable for non-expert users.

翻译：数据库是企业最关键的资产，然而对于做出最重要决策的人员而言，其可访问性仍然严重不足。本文介绍了Tursio搜索平台，该平台通过在底层数据库之上构建抽象层（即语义知识图谱），使其能够通过自然语言进行搜索。Tursio将大型语言模型（LLMs）融入查询处理栈的每个环节，包括数据建模、查询编译、查询规划与结果推理。这使得Tursio能够基于传统查询规划与重写技术（而非黑箱记忆机制）系统化处理自然语言查询。我们详细阐述了Tursio的体系架构，并在实际生产负载、合成基准测试及真实场景基准测试上进行了全面评估。实验结果表明，Tursio在保持高效性与可扩展性的同时实现了高准确率，真正为非专业用户实现了数据库的可搜索化。