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

TL;DR

This paper introduces an agentic pipeline that enhances natural-language-to-SQL systems with planning and reasoning capabilities, significantly improving accuracy and usability for complex spatio-temporal database queries.

Contribution

The paper presents a novel agentic orchestration approach that extends a basic text-to-SQL model with planning, decomposition, and schema inspection, enabling better handling of complex queries.

Findings

Achieved 91.4% accuracy on complex spatio-temporal queries.

Significantly outperformed naive baseline with 28.6% accuracy.

Enhanced user interaction through maps, plots, and summaries.

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.