AskDB: An LLM Agent for Natural Language Interaction with Relational Databases
Xuan-Quang Phan, Tan-Ha Mai, Thai-Duy Dinh, Minh-Thuan Nguyen, Lam-Son L\^e

TL;DR
AskDB is an LLM-based agent that enables natural language interaction with relational databases, supporting both data analysis and administrative tasks through innovative schema-aware prompting and task decomposition.
Contribution
It introduces a unified system combining data analysis and admin operations using a large language model with novel schema-aware prompting and task planning mechanisms.
Findings
Strong performance on Text-to-SQL benchmark
Effective handling of DBA tasks
Autonomous debugging and web retrieval capabilities
Abstract
Interacting with relational databases remains challenging for users across different expertise levels, particularly when composing complex analytical queries or performing administrative tasks. Existing systems typically address either natural language querying or narrow aspects of database administration, lacking a unified and intelligent interface for general-purpose database interaction. We introduce AskDB, a large language model powered agent designed to bridge this gap by supporting both data analysis and administrative operations over SQL databases through natural language. Built on Gemini 2, AskDB integrates two key innovations: a dynamic schema-aware prompting mechanism that effectively incorporates database metadata, and a task decomposition framework that enables the agent to plan and execute multi-step actions. These capabilities allow AskDB to autonomously debug derived SQL,…
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Taxonomy
TopicsAdvanced Database Systems and Queries · Semantic Web and Ontologies · Personal Information Management and User Behavior
