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RAG Blueprint Agent Skill

A single agent skill that enables AI coding assistants (Claude Code, Cursor, Codex, etc.) to deploy, configure, troubleshoot, and manage the NVIDIA RAG Blueprint autonomously.

Installation

npx skills add .

Select rag-blueprint — it includes all capabilities (deploy, configure, shutdown, troubleshoot) in one skill.

Architecture: Skills = Process, Docs = Truth

SKILL.md           = ROUTER (intent detection, autonomy rules, configure routing table)
Reference files    = WHAT/HOW (deployment workflows, feature playbooks, diagnostics)
docs/*.md          = SOURCE OF TRUTH (never copied into skills)
notebooks/*.ipynb  = RUNNABLE EXAMPLES (referenced from relevant skills)

The SKILL.md detects user intent and routes to the correct reference file. Reference files are concise playbooks that point to docs/*.md for detailed configuration — this prevents staleness from duplicated content.

Skill Structure

skill-source/.agents/skills/rag-blueprint/
  SKILL.md                              ← Single entry point (intent router)
  references/
    deploy.md                           ← Deployment: env analysis, NGC key, routing
    deploy/
      docker.md                         ← Docker Compose deployment workflow
      docker-self-hosted.md             ← Self-hosted NIMs (local GPU inference)
      docker-nvidia-hosted.md           ← Cloud NIMs (NVIDIA API endpoints)
      docker-retrieval-only.md          ← Search/retrieve only (no LLM)
      helm.md                           ← Kubernetes / Helm deployment workflow
      helm-standard.md                  ← Standard Helm chart deployment
      helm-mig.md                       ← Multi-Instance GPU deployment
      library.md                        ← Python library mode workflow
      library-full.md                   ← Python API + Docker backend
      library-lite.md                   ← Containerless (Milvus Lite + cloud APIs)
    configure/
      vlm.md                            ← VLM, VLM embeddings, image captioning
      guardrails.md                     ← NeMo Guardrails
      query-and-conversation.md         ← Query rewriting, decomposition, multi-turn
      ingestion.md                      ← Text-only, audio, Nemotron Parse, OCR, batch CLI
      search-and-retrieval.md           ← Hybrid search, multi-collection, metadata, filters
      models-and-infrastructure.md      ← Model changes, vector DB, auth, API keys, profiles
      reasoning-and-generation.md       ← Reasoning, self-reflection, prompts, generation params
      summarization.md                  ← Document summarization during ingestion
      observability.md                  ← Tracing, Zipkin, Grafana, Prometheus
      multimodal-query.md              ← Image + text querying with VLM embeddings
      data-catalog.md                   ← Collection/document metadata management
      user-interface.md                 ← RAG UI settings and usage
      api-reference.md                  ← REST API endpoints and schemas
      evaluation.md                     ← RAGAS quality metrics
      mcp.md                            ← MCP server & client tools
      migration.md                      ← Version upgrade guide
      notebooks.md                      ← Notebook environment and catalog
    shutdown.md                         ← Stop and tear down services
    troubleshoot.md                     ← Diagnose and fix common issues

How It Works

  1. User says "deploy RAG" → SKILL.md routes to references/deploy.md → env analysis → routes to deploy/docker.md, deploy/helm.md, or deploy/library.md
  2. User says "enable VLM" → SKILL.md routes to references/configure/vlm.md → reads docs/vlm.md for detailed steps
  3. User says "RAG is broken" → SKILL.md routes to references/troubleshoot.md → auto-triage diagnostic sweep
  4. User says "stop RAG" → SKILL.md routes to references/shutdown.md → detects and stops all services

Supported Deployment Modes

Read docs/support-matrix.md for current hardware requirements per mode.

Mode Docker Required Description
Docker (self-hosted) Yes Full on-prem with local NIM inference
Docker (NVIDIA-hosted) Yes Cloud APIs for model inference
Docker (retrieval-only) Yes No LLM, search/retrieve only
Helm / Kubernetes No (K8s) Production K8s with NIM Operator
Library (full) Yes (backend) Python API with Docker backend services
Library (lite) No Milvus Lite + cloud APIs, zero infrastructure

NGC_API_KEY Handling

Skills never expose the API key value to the LLM. The approach:

  1. Check if NGC_API_KEY is set: [ -n "$NGC_API_KEY" ] && echo "SET" || echo "NOT_SET"
  2. If not set, ask the user to run export NGC_API_KEY="nvapi-your-key" in the terminal
  3. For docker login, the user runs it themselves (the command expands the key)
  4. As a fallback, offer to write a placeholder to deploy/compose/.env for the user to replace

Notebook Integration

All 13 notebooks are referenced from relevant reference files:

Notebook Referenced In
ingestion_api_usage.ipynb references/configure/ingestion.md
retriever_api_usage.ipynb references/configure/search-and-retrieval.md
image_input.ipynb references/configure/vlm.md, references/configure/multimodal-query.md
summarization.ipynb references/configure/summarization.md
evaluation_01_ragas.ipynb references/configure/evaluation.md
evaluation_02_recall.ipynb references/configure/evaluation.md
nb_metadata.ipynb references/configure/search-and-retrieval.md
rag_library_usage.ipynb references/deploy/library-full.md
rag_library_lite_usage.ipynb references/deploy/library-lite.md
building_rag_vdb_operator.ipynb references/configure/models-and-infrastructure.md
mcp_server_usage.ipynb references/configure/mcp.md
nat_mcp_integration.ipynb references/configure/mcp.md
launchable.ipynb SKILL.md