In this guide, we will take you through setting up a RAG pipeline. We will utilize open source tools such as Charmed OpenSearch for efficient search retrieval and KServe for machine learning inference, specifically in Azure and Ubuntu environments while leveraging silicons.

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This post provides a comprehensive guide on setting up a retrieval-augmented generation (RAG) pipeline using open source tools like Charmed OpenSearch and KServe, specifically in Azure and Ubuntu environments. It covers key components of a RAG system, including data processing, embedding models, retrieval, vector databases, and more, while emphasizing the importance of scalability, security, and the use of open source solutions.

Building an end-to-end Retrieval- Augmented Generation (RAG) workflow