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Background The paper discussed the necessity of domain-specific knowledge for database diagnosis and how even LLMs like GPT-4 fail to master this domain knowledge directly, resulting in less than 50% accuracy. Challenges included enhancing LLMS' understanding of the diagnosis problem, improving diagnosis performance for single-cause anomalies, and enhancing diagnosis capability for multi-cause anomalies.
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Existing Work Existing methods lack customized solutions for database diagnosis, and current LLMs do not have the optimizations and guidance needed for diagnosing databases, leading to poor performance in this domain.
- Introduced a database diagnosis system based on LLM (D-Bot)
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Challenge 1: Enhancing understanding of the diagnosis problem D-Bot tackles this challenge by enhancing LLMs’ understanding through knowledge extraction from documents, automatic prompt generation, and root cause analysis using a tree search algorithm. This includes dealing with knowledge extraction from long documents, matching the right knowledge based on the context, and retrieving potentially useful tools.
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Challenge 2: Improving diagnosis performance for single-cause anomalies To tackle single-cause anomalies, D-Bot employs knowledge-and-tool prompts along with a tree-search-based diagnosis approach to guide LLMs in logical analysis and reasoning. This involves overcoming LLM's early stop problem and "hallucination" issues, and utilizing strategies to guide in-depth and reasonable LLM analysis.
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Challenge 3: Enhancing diagnosis capability for multi-cause anomalies For complex multi-cause anomalies, D-Bot designed an efficient diagnosis mechanism where multiple LLMs collaboratively tackle complex database problems, improving diagnosis accuracy and efficiency through cross-reviews.
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D-Bot leverages LMM-related technologies, such as prompting generation and fine-tuning, to enhance its capabilities in database diagnosis. The architecture and components of D-Bot were showcased, including detailed explanations of how to prepare the knowledge and tools necessary for diagnosis. In real benchmarks (including 539 anomalies from six typical applications), D-Bot significantly outperformed traditional methods and vanilla models like GPT-4, effectively analyzing root causes of unseen anomalies.
D-Bot is a database diagnosis system based on large language models designed to improve the efficiency and accuracy of database diagnosis by extracting knowledge from documents and generating effective diagnosis reports, addressing challenges faced by domain experts in the field of database diagnosis.