ChatDBA: An AI‑Powered Database Fault Diagnosis Assistant Using Retrieval‑Augmented Generation

ChatDBA is a smart assistant for database operation developed by Shanghai Aikesheng, using conversational interaction to offer database fault diagnosis, professional knowledge learning, SQL generation and optimization, aiming to improve DBA efficiency.

The system is built on a Retrieval‑Augmented Generation (RAG) framework that initially suffered from generic outputs, weak logical coherence, and difficulty handling complex multi‑turn troubleshooting.

To overcome these issues, the architecture was redesigned with components for environment input processing, model planning using decision trees, chain‑of‑thought, and reflection, and tool usage for retrieval APIs and monitoring interfaces.

A fault‑diagnosis logic tree is introduced to structure multi‑turn dialogues, categorizing error scenarios and dynamically updating the tree as new information arrives, thereby enhancing the accuracy and speed of problem resolution.

Advanced retrieval strategies are employed, including multi‑path recall (keyword + vector), query rewriting and expansion, multimodal retrieval for text, images, and tables, graph‑based RAG, and document formatting that extracts fault phenomena, causes, methods, and solutions.

The preprocessing pipeline cleans historical work orders, classifies and formats them, scores the formatted content, and uses model‑based evaluation to ensure completeness before feeding into the RAG system.

Core features of ChatDBA comprise key‑information extraction from diverse inputs, NL2SQL‑based SQL generation and optimization, and a knowledge‑learning module to help DBAs continuously improve.

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