Chat with Me and Get the PromQL You Want - Kunyi Chen, Alibaba Cloud
与我聊天并获取您想要的PromQL | Chat with Me and Get the PromQL You Want - Kunyi Chen, Alibaba Cloud
本次演讲介绍了一种解决SRE和开发人员在使用Prometheus监控系统操作时面临的挑战的解决方案。Prometheus是一种在可观察性领域广泛使用的开源时间序列数据库。尽管PromQL查询能够满足几乎任何场景的监控需求,但非SRE开发人员、管理人员和产品经理可能会发现由于复杂的语法以及在Prometheus中混淆的标签和指标名称,编写准确的PromQL查询以检索所需数据是具有挑战性的。为了解决这个问题,我们开发了一个使用LLMs的聊天机器人,将自然语言转换为PromQL,消除了阅读文档或查询指标含义的需要。我们将演示如何使用本地知识库和提示工程技巧来优化LLMs以满足需求,并进行演示。我们的解决方案为SRE和开发人员提供了准确高效的替代方案。
This talk presents a solution for the challenges faced by SREs and developers when monitoring system operations using Prometheus, a widely-used open-source time-series database in the observability field. Although PromQL queries are capable of meeting the monitoring requirements of almost any scenario, non-SRE developers, managers, and PDs may find it challenging to write accurate PromQL queries to retrieve the desired data due to the complex syntax and confusing labels and metrics names in Prometheus To address this, we developed a chat bot using LLMs that converts natural language into PromQL, eliminating the need to read documentation or query metric meanings. We will demonstrate how LLMs can be optimized using local knowledge bases and prompt engineering skills to meet requirements, followed by a demo. Our solution provides an accurate and efficient alternative for SRE and developers.
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