AskDB: An LLM Agent for Natural Language Interaction with Relational Databases

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, retrieve contextual information via real-time web search, and adaptively refine its responses. We evaluate AskDB on a widely used Text-to-SQL benchmark and a curated set of DBA tasks, demonstrating strong performance in both analytical and administrative scenarios. Our results highlight the potential of AskDB as a unified and intelligent agent for relational database systems, offering an intuitive and accessible experience for end users.