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

• URL: http://arxiv.org/abs/2511.16131v1
• Date: Thu, 20 Nov 2025 08:06:09 GMT
• Title: AskDB: An LLM Agent for Natural Language Interaction with Relational Databases
• Authors: Xuan-Quang Phan, Tan-Ha Mai, Thai-Duy Dinh, Minh-Thuan Nguyen, Lam-Son Lê,
• Abstract summary: We introduce AskDB, a large language model powered agent for interacting with databases through natural language.<n>AskDB supports both data analysis and administrative operations oversql databases through natural language.<n>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.
• Score: 0.06524460254566904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• TEXT2DB: Integration-Aware Information Extraction with Large Language Model Agents [64.11547566154947]
  We propose a new formulation of IE TEXT2DB that emphasizes the integration of IE output and the target database.<n>We introduce a new benchmark featuring common demands such as data infilling, row population, and column addition.<n>Experiments show that OPAL can successfully adapt to diverse database schemas by generating different code plans and calling the required IE models.
  arXiv  Detail & Related papers  (2025-10-28T02:49:40Z)
• BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation for Large Language Models via Lens of Dynamic Interactions [33.59162905707337]
  Large language models (LLMs) have demonstrated remarkable performance on single-turn text-to- tasks, but real-world database applications predominantly require multi-turn interactions.<n>Existing multi-turn benchmarks fall short by treating conversation histories as static context or limiting evaluation to read-only operations.<n>We introduce BIRD-INTERACT, a benchmark that restores this realism through: (1) a comprehensive interaction environment coupling each database with a knowledge base, metadata files, and a function-driven user simulator, enabling models to solicit clarifications, retrieve knowledge, and recover from errors without human supervision; (2) two evaluation settings consisting of a
  arXiv  Detail & Related papers  (2025-10-06T19:31:47Z)
• Text2Schema: Filling the Gap in Designing Database Table Structures based on Natural Language [22.15408079332362]
  People without a database background usually rely on file systems or tools such as Excel data management.<n> Database systems possess strong management capabilities, but require a high level of professional expertise from users.
  arXiv  Detail & Related papers  (2025-03-31T09:39:19Z)
• DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL [18.915121803834698]
  We propose DB-Explore, a novel framework that systematically aligns large language models with database knowledge.<n>Our framework enables comprehensive database understanding through diverse sampling strategies and automated instruction generation.
  arXiv  Detail & Related papers  (2025-03-06T20:46:43Z)
• E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL [1.187832944550453]
  We introduce E-Seek, a novel pipeline specifically designed to address these challenges through direct schema linking and candidate predicate augmentation.<n>E-Seek enhances the natural language query by incorporating relevant database items (i.e., tables, columns, and values) and conditions directly into the question andsql construction plan, bridging the gap between the query and the database structure.<n> Comprehensive evaluations illustrate that E-Seek achieves competitive performance, particularly excelling in complex queries with a 66.29% execution accuracy on the test set.
  arXiv  Detail & Related papers  (2024-09-25T09:02:48Z)
• Text2SQL is Not Enough: Unifying AI and Databases with TAG [47.45480855418987]
  Table-Augmented Generation (TAG) is a paradigm for answering natural language questions over databases. We develop benchmarks to study the TAG problem and find that standard methods answer no more than 20% of queries correctly.
  arXiv  Detail & Related papers  (2024-08-27T00:50:14Z)
• Hybrid Querying Over Relational Databases and Large Language Models [8.926173054003547]
  We present the first cross-domain benchmark, SWAN, containing 120 beyond-Database questions over four real-world databases. We present two solutions: one based on schema expansion and the other based on user defined functions. Our evaluation demonstrates that using GPT-4 Turbo with few-shot prompts, one can achieves up to 40.0% in execution accuracy and 48.2% in data factuality.
  arXiv  Detail & Related papers  (2024-08-01T19:29:18Z)
• Relational Database Augmented Large Language Model [59.38841050766026]
  Large language models (LLMs) excel in many natural language processing (NLP) tasks. They can only incorporate new knowledge through training or supervised fine-tuning processes. This precise, up-to-date, and private information is typically stored in relational databases.
  arXiv  Detail & Related papers  (2024-07-21T06:19:10Z)
• DBCopilot: Natural Language Querying over Massive Databases via Schema Routing [47.009638761948466]
  We present DBCopilot, a framework that addresses challenges by employing a compact and flexible copilot model for routing over massive databases.<n>This framework utilizes a single lightweight differentiable search index to construct semantic mappings for massive database schemata, and navigates natural language questions to their target databases and tables in a relation joint retrieval manner.
  arXiv  Detail & Related papers  (2023-12-06T12:37:28Z)
• SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended) [53.95151604061761]
  This paper introduces the framework for enhancing Text-to- filtering using large language models (LLMs) With few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error analyses. With instruction fine-tuning, we delve deep in understanding the critical paradigms that influence the performance of tuned LLMs.
  arXiv  Detail & Related papers  (2023-05-26T21:39:05Z)
• AskYourDB: An end-to-end system for querying and visualizing relational databases using natural language [0.0]
  We propose a semantic parsing approach to address the challenge of converting complex natural language into SQL. We modified state-of-the-art models, by various pre and post processing steps which make the significant part when a model is deployed in production. To make the product serviceable to businesses we added an automatic visualization framework over the queried results.
  arXiv  Detail & Related papers  (2022-10-16T13:31:32Z)
• "What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL [49.85635994436742]
  We include human in the loop and present a novel-independent interactive approach (PIIA) that interacts with users using multi-choice questions. PIIA is capable of enhancing the text-to-domain performance with limited interaction turns by using both simulation and human evaluation.
  arXiv  Detail & Related papers  (2020-11-09T02:14:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.