KubeCon + CloudNativeCon + Open Source Summit + AI_dev China 2024

In day-to-day work, both SREs and developers often struggle when working with the observability tools like Prometheus, mainly due to the complex PromQL syntax and disorganized metrics. This talk will showcase how to build Agent. It will have the ability to think, act, and analyze like a human, and it will solve user issues through conversation. This talk presents two main standout ideas: 1. Leveraging RAG technology, it performs multi-path retrieval from local metric knowledge, Prometheus API, Request Logs, and public domain knowledge to produce a consolidated answer. 2. Using the ReAct method, it engages in multi-round dialogues to refine and generate the correct PromQL, call api, and render the dashboard return. This talk, we hope the audience will learn: 1. How to integrate LLM effectively within the observability space. 2. The steps to create an easy-to-use and practical Prometheus AI Agent. 3. Gain experience and insights from practical examples of the Prometheus AI Agent.

在日常工作中，SRE和开发人员在使用像Prometheus这样的可观察性工具时经常遇到困难，主要是由于复杂的PromQL语法和混乱的指标。本次演讲将展示如何构建Agent。它将具有像人类一样思考、行动和分析的能力，并通过对话解决用户问题。 本次演讲提出了两个主要的突出想法： 1. 利用RAG技术，从本地度量知识、Prometheus API、请求日志和公共领域知识中进行多路径检索，以生成一个整合的答案。 2. 使用ReAct方法，进行多轮对话以完善和生成正确的PromQL，调用api，并呈现仪表板返回。 通过本次演讲，我们希望观众能学到： 1. 如何在可观察性领域有效地整合LLM。 2. 创建一个易于使用和实用的Prometheus人工智能Agent的步骤。 3. 从Prometheus人工智能Agent的实际示例中获得经验和见解。