Build a distributed RAG pipeline with Ray Data on OpenShift AI

This blog post presents a detailed guide on building a distributed retrieval-augmented generation (RAG) pipeline using Ray Data on OpenShift AI. It addresses the inefficiencies of traditional document processing by implementing streaming execution to concurrently manage the stages of parsing documents, generating embeddings, and storing vectors. The article emphasizes the importance of structure-aware parsing and demonstrates how the pipeline can significantly improve performance while maintaining high GPU utilization. It includes practical advice on configuring the pipeline and discusses the technology stack involved.