From Queries to Insights: Agentic LLM Pipelines for Spatio-Temporal Text-to-SQL

• URL: http://arxiv.org/abs/2510.25997v1
• Date: Wed, 29 Oct 2025 22:18:57 GMT
• Title: From Queries to Insights: Agentic LLM Pipelines for Spatio-Temporal Text-to-SQL
• Authors: Manu Redd, Tao Zhe, Dongjie Wang,
• Abstract summary: We present a naive text-to-Act baseline (Rellama-sqlcoder-8b) with orchestration by a Mistral-based Rellama-sqlcoder-8b.<n>We evaluate on 35 natural-language queries over the NYC and Tokyo check-in, covering spatial, temporal multi-dataset reasoning.<n>The agent achieves substantially higher accuracy than the dataset 91.4% vs. 28.6% and enhances usability through maps, and plots structured natural-language summaries.
• Score: 8.496933324334167
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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