📄 中文摘要
在前两部分中，Ollama与phi3:mini的设置完成，并通过Prometheus和Grafana进行监控。尽管模型已运行，但仅限于其训练数据。为了解决这一问题，构建了一个RAG（检索增强生成）管道，使模型能够回答有关用户文档、配置和剧本的问题。RAG的核心在于通过检索相关信息来增强生成能力，克服模型对特定基础设施知识的缺乏，从而提高其在特定环境中的实用性。
AI家庭实验室 — 第三部分：构建RAG管道：让本地AI真正了解你的内容
📄 English Summary
AI Home Lab — Part 3: Building a RAG Pipeline: Making Your Local AI Actually Know Your Stuff
In the previous parts, Ollama was set up with phi3:mini and monitored using Prometheus and Grafana. While the model was operational, it was limited to its training data. To address this limitation, a Retrieval-Augmented Generation (RAG) pipeline was built, allowing the model to answer questions related to user-specific documents, configurations, and playbooks. The essence of RAG lies in enhancing the model's generative capabilities by retrieving relevant information, thus overcoming the lack of knowledge about specific infrastructure and improving its utility in tailored environments.