Natural-language-to-SQL (NL-to-SQL) systems hold promise for democratizing access to structured data, allowing users to query databases without learning SQL. Yet existing systems struggle with realistic spatio-temporal queries, where success requires aligning vague user phrasing with schema-specific categories, handling temporal reasoning, and choosing appropriate outputs. We present an agentic pipeline that extends a naive text-to-SQL baseline (llama-3-sqlcoder-8b) with orchestration by a Mistral-based ReAct agent. The agent can plan, decompose, and adapt queries through schema inspection, SQL generation, execution, and visualization tools. We evaluate on 35 natural-language queries over the NYC and Tokyo check-in dataset, covering spatial, temporal, and multi-dataset reasoning. The agent achieves substantially higher accuracy than the naive baseline 91.4% vs. 28.6% and enhances usability through maps, plots, and structured natural-language summaries. Crucially, our design enables more natural human-database interaction, supporting users who lack SQL expertise, detailed schema knowledge, or prompting skill. We conclude that agentic orchestration, rather than stronger SQL generators alone, is a promising foundation for interactive geospatial assistants.

翻译：自然语言到SQL（NL-to-SQL）系统有望实现结构化数据的民主化访问，使用户无需学习SQL即可查询数据库。然而，现有系统在处理现实的时空查询时面临挑战，这些查询的成功需要将模糊的用户表述与特定模式类别对齐、处理时间推理并选择合适的输出。我们提出了一种智能流水线，通过基于Mistral的ReAct智能体进行编排，扩展了基础的文本到SQL基线模型（llama-3-sqlcoder-8b）。该智能体能够通过模式检查、SQL生成、执行和可视化工具来规划、分解和调整查询。我们在纽约和东京签到数据集上评估了35个自然语言查询，涵盖空间、时间和多数据集推理。该智能体相比基础基线实现了显著更高的准确率（91.4% vs. 28.6%），并通过地图、图表和结构化自然语言摘要提升了可用性。关键的是，我们的设计实现了更自然的人机数据库交互，支持缺乏SQL专业知识、详细模式知识或提示技能的用户。我们得出结论：智能编排（而非仅依赖更强的SQL生成器）是构建交互式地理空间助手的有前景的基础。