ChatDBA: An AI‑Powered Intelligent Assistant for Database Fault Diagnosis and Management

ChatDBA, created by Shanghai Aikesheng, is an intelligent conversational assistant for database administrators that offers fault diagnosis, professional knowledge learning, SQL generation and optimization, aiming to improve DBA work efficiency.

The rapid rise of large language models has spurred AI applications in the database field, such as BI analysis and NL2SQL, prompting the development of ChatDBA to help DBAs handle daily tasks more effectively.

The initial RAG‑based prototype suffered from generic answers, weak logic, and misaligned troubleshooting approaches; consequently, a redesigned architecture was built, still based on RAG but enriched with environment input processing, model planning using decision trees, chain‑of‑thought reasoning, and tool integration to ensure logical, coherent multi‑turn dialogues.

To enhance retrieval, ChatDBA employs multi‑path recall (keyword + vector), query rewriting/expansion, multimodal retrieval for text, images, and tables, vertical‑domain augmentation, and graph‑RAG for multi‑hop knowledge extraction.

Document processing involves cleaning raw tickets, classifying and formatting them into fault phenomenon, cause, method and solution sections, scoring the reformatted chunks, and using LLMs to rewrite or reject low‑quality content, thereby preserving essential information.

Memory challenges are addressed by summarizing long conversations, retaining recent rounds, and supplementing context through real‑time retrieval, mitigating the model’s input length limits.

Intent recognition is handled via predefined intent templates and multi‑intent processing, allowing the system to merge answers from different intent branches for more accurate responses.

Observability and evaluation are achieved by constructing an observable model pipeline, designing evaluation frameworks, and exploring knowledge‑graph‑based explainable metrics for stable, end‑to‑end output.

Future directions include multimodal ticket handling, real‑time monitoring integration, comprehensive database knowledge‑graph construction, and personalized recommendation of learning materials and troubleshooting solutions.

Key features of ChatDBA comprise a key‑information extraction module, NL2SQL‑based SQL generation and optimization, and a knowledge‑learning component to support continuous DBA skill improvement.