How to Deploy the NVIDIA RAG Blueprint on Kubernetes with Helm

This article provides a detailed technical tutorial on deploying the NVIDIA RAG Blueprint (version 2.6.0) on Kubernetes with Helm. It explains that the Blueprint is a coordinated retrieval platform comprising ingestion services, RAG servers, NVIDIA NIM microservices, NV-Ingest, a vector database (Elasticsearch by default), object storage (SeaweedFS), model caches, and Kubernetes operators. The guide covers prerequisites like GPU and Kubernetes checks, storage classes, secrets, and pinned chart versions. It emphasizes validating ingestion and retrieval separately, adding hybrid search and reranking after baseline success, and treating MIG as a capacity decision. The article is highly relevant to IT/technology, focusing on DevOps, Kubernetes deployment, and AI infrastructure.