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sayalinvidia
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rag-blueprint

Deploy NVIDIA RAG Blueprint across self-hosted, NVIDIA-hosted, or retrieval-only modes.

Updated Apr 16, 2026
Search Tags:#deployment#rag#docker#infrastructure#helm#guardrails
One-click install
npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill rag-blueprint-sayalinvidia
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rag-blueprint
Source: https://github.com/sayalinvidia/sayali-skills-test/tree/main/skills/rag-blueprint
Command: npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill rag-blueprint-sayalinvidia

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires bash, docker, kubectl, helm, and includes references (resource) components.

What problem does it solve?

NVIDIA RAG Blueprint provides an end-to-end framework to deploy, configure, and govern NVIDIA RAG pipelines across on-prem and cloud environments, enabling consistent governance, safety checks, and repeatable deployments.

Core Features & Use Cases

  • Guided environment analysis and mode detection (self-hosted, NVIDIA-hosted, or retrieval-only) with automated health checks.
  • Unified deployment workflows for Docker Compose, Kubernetes Helm, and library-based setups, plus centralized references for configuration.
  • Per-feature configurability (VLM, guardrails, query/rewrite, ingestion, observability) and easy switch between deployment modes.
  • Use cases include production-grade RAG deployments, migration between deployment modes, and governance-focused operations.

Quick Start

Follow the deployment workflow in references/deploy/docker-self-hosted.md or references/deploy/docker-nvidia-hosted.md to begin.

Frequently Asked Questions about rag-blueprint

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I deploy the NVIDIA RAG blueprint using Docker or Helm?▼

Deploy the NVIDIA RAG blueprint using guided workflows for Docker Compose or Kubernetes Helm, which include environment analysis, staged deployment, health checks, and rollback-ready steps.

What is the difference between self-hosted and NVIDIA-hosted RAG deployment modes?▼

Self-hosted mode runs RAG infrastructure locally via Docker or Helm, while NVIDIA-hosted shifts compute to cloud endpoints; both modes are managed through a unified workflow with automated mode detection.

Can I enable guardrails and VLM features selectively in my RAG pipeline?▼

Yes, the RAG blueprint supports per-feature configurability, allowing you to independently toggle VLM, guardrails, query rewriting, ingestion, and observability features during deployment.

Do I need kubectl and helm installed to use the RAG blueprint?▼

Yes, Kubernetes deployments require kubectl and helm, while local Docker deployments require bash and docker; the skill checks prerequisites and blockers before initiating the staged deployment.

How does the RAG blueprint handle rollback if a deployment fails health checks?▼

The RAG blueprint enforces governance through staged deployment with health verifications, ensuring each step is rollback-ready if blocker checks or health validations fail during the process.

What is the best way to migrate a RAG deployment from local Docker to Kubernetes?▼

Use the unified deployment workflow to switch between modes, leveraging environment analysis and mode detection to transition RAG pipelines from Docker Compose to Kubernetes Helm setups.

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