RAG Blueprint

NVIDIA RAG Blueprint

Purpose

Use this skill for NVIDIA RAG Blueprint operations: deployment, configuration, troubleshooting, shutdown, and feature management across Docker, Helm, and library deployments.

Instructions

1. Match the user request to the intent routing table below.
2. Read the referenced playbook before making changes.
3. Use repository docs and deployment config files as the source of truth.
4. Verify the affected service or workflow after changes.

Prerequisites

• NVIDIA RAG Blueprint repository checkout.
• Docker/Compose or Kubernetes/Helm for deployments.
• Python 3.11+ for library workflows.
• NVIDIA GPU tooling for self-hosted NIM services.

Autonomy Principles

• Auto-detect everything: GPU, VRAM, drivers, Docker, CUDA, disk, OS, ports, existing services, NGC key, repo state.
• If it can be checked with a command, check it — don't ask the user.
• Ask only when user action is required: providing an API key, confirming data deletion, or choosing between equally valid options.
• Once analysis is done, route to the correct workflow and execute.

Intent Detection

Determine what the user wants and route immediately:

If the intent is ambiguous, infer from context (e.g., "RAG isn't working" → troubleshoot; "get RAG running" → deploy). Only ask if genuinely unclear.

Configure

Requires a running RAG deployment. If services are not running, deploy first via references/deploy.md.

Match the user's request to a reference file, then read and follow it:

Configure Flow

1. Match the user's request to a reference file from the table above.

2. Detect what's running:

   echo "=== NIM ===" && docker ps --format '{{.Names}}' 2>/dev/null | grep -iE '(nim-llm|nemotron-(vlm-)?embedding|nemotron-ranking|nemotron-vlm|nemotron-3-nano-omni|page-elements|graphic-elements|table-structure|nemotron-ocr)' || echo "NO_LOCAL_NIMS"; echo "=== RAG ===" && docker ps --format '{{.Names}}' 2>/dev/null | grep -iE '(rag-server|ingestor-server|elasticsearch|milvus|seaweedfs|lancedb)' || echo "NO_DOCKER_RAG"; echo "=== K8S ===" && kubectl get pods -n rag 2>/dev/null | head -5 || echo "NO_K8S"; echo "=== LIBRARY ===" && ps aux 2>/dev/null | grep -E '(nvidia_rag|uvicorn.*rag)' | grep -v grep || echo "NO_LIBRARY"
   

3. Use this table to determine platform, deployment type, and where config lives:

   Local NIMs running?	RAG services running?	Deployment Type	Config Location
   Yes (Docker)	Any	Self-hosted	deploy/compose/.env
   No	Yes (Docker)	NVIDIA-hosted	deploy/compose/nvdev.env
   Yes (K8s pods)	Any	Self-hosted	values.yaml (NIM sections)
   No	Yes (K8s pods)	NVIDIA-hosted	values.yaml (envVars)
   —	Library processes	Library mode	notebooks/config.yaml
   No	No	Not running	Deploy first via references/deploy.md

   Tell the user what you detected and ask to confirm. Example: "I see local NIM containers running (nim-llm-ms, nemotron-vlm-embedding-ms) — this is a self-hosted deployment. Config file is deploy/compose/.env. Correct?"

4. Check current feature state before changing anything — read the config location from step 3, then cross-check the live service:

   • Docker: docker exec rag-server env 2>/dev/null | grep -E "<VAR_NAME>"
   • Helm: kubectl get pod -n rag -l app=rag-server -o jsonpath='{.items[0].spec.containers[0].env}' 2>/dev/null

   If the config file and live service disagree, tell the user the service has stale config and will need a restart.

5. If the feature needs extra GPUs, check availability against hardware restrictions (see below):

   nvidia-smi --query-gpu=index,name,memory.total,memory.used --format=csv,noheader 2>/dev/null || echo "NO_GPU"
   

6. Read the reference file and apply changes:

   • Docker: edit the env file (uncomment to enable, re-comment to disable — the env file is the source of truth). Then restart the affected service:

     source <env-file> && docker compose -f deploy/compose/<compose-file> up -d
     

     Service	Compose File
     rag-server	docker-compose-rag-server.yaml
     ingestor-server	docker-compose-ingestor-server.yaml
     Elasticsearch, Milvus, etcd, SeaweedFS	vectordb.yaml
     NIM containers (LLM, embedding, ranking, VLM, OCR, parse, audio, extraction)	nims.yaml
     guardrails	docker-compose-nemo-guardrails.yaml
     observability (Grafana, Prometheus, Zipkin)	observability.yaml

   • Helm: edit values.yaml, then upgrade: helm upgrade rag <chart> -n rag -f values.yaml
   • Library: edit notebooks/config.yaml, then restart the Python process

7. Verify:

   • Docker: docker ps --format "table {{.Names}}\t{{.Status}}" | head -20; curl -s http://localhost:8081/v1/health?check_dependencies=true 2>/dev/null | head -1
   • Helm: kubectl get pods -n rag; kubectl rollout status deployment/rag-server -n rag --timeout=120s
   • Library: curl -s http://localhost:8081/v1/health 2>/dev/null | head -1

8. If restart fails, read references/troubleshoot.md. If multiple features requested, repeat from step 1 for each.

Examples

• "Deploy RAG" -> route to references/deploy.md.
• "Enable VLM" -> route to references/configure/vlm.md.
• "RAG is unhealthy" -> route to references/troubleshoot.md.
• "Stop RAG" -> route to references/shutdown.md.

Limitations

• Operational guidance only applies to this RAG Blueprint repository.
• Live deployment changes require a running Docker, Helm, or library target.
• Secrets such as NGC_API_KEY must be supplied by the user environment.

Troubleshooting

When User Says "Configure" Without Specifics

Run steps 2–3 above, then read the identified config file to list what's currently enabled:

grep -E "^(export )?(ENABLE_|APP_)" <config-file> 2>/dev/null | sort

Summarize what's running and enabled, then ask which feature to change.

Hardware Restrictions

Read docs/support-matrix.md for current GPU requirements per deployment mode. Read docs/service-port-gpu-reference.md for port mappings and GPU assignments.

Anti-Patterns

• Changing deployment knobs before identifying the active deployment mode: Compose, Helm, and library paths are not interchangeable.
• Treating retrieval, model, and infrastructure faults as the same class of problem: It wastes time and can hide the real failing layer.
• Stopping or tearing down services without checking persistence impact: Cleanup can destroy the exact evidence needed for recovery.

Verification Protocol

Before claiming "skill applied successfully":

1. Pass/fail: The workflow identifies the active deployment path and uses the matching upstream playbook before proposing changes.
2. Pass/fail: Any configuration change is tied to the exact file, chart value, or environment variable that owns the behavior.
3. Pass/fail: Health checks, logs, or a real retrieval request are used before claiming the stack is healthy again.
4. Pressure-test scenario: Apply the workflow to a half-running deployment where ingestion works but retrieval answers are empty.
5. Success metric: The requested RAG feature or service state is reproducible, observable, and verified with a live check path.

Cross-Client Portability

This skill is written to stay usable across GitHub Copilot, Claude Code, and Codex.

• GitHub Copilot: keep the folder in a Copilot-visible skill path or wrap the workflow in project instructions when folder discovery is unavailable.
• Claude Code: keep the folder in a local skills directory or a compatible plugin source.
• Codex: install or sync the folder into $CODEX_HOME/skills/rag-blueprint and restart Codex after major changes.

MCP Availability And Fallback

Preferred MCP Server: None required

• Fallback prompt: "Use the rag-blueprint skill without MCP. Rely on the local SKILL.md, bundled references or scripts, and manual verification. Show the exact commands, evidence, and final checks you used before concluding."
• If the current host does not expose a matching server, use the bundled references, scripts, native toolchain, and manual workflow already described in this skill.
• Treat direct local verification, rendered output, logs, tests, or screenshots as the fallback evidence path before completion.

Related Skills

• cloud-design-patterns: Use it when the RAG deployment decision also needs broader distributed-system tradeoff analysis.
• devops-tooling: Use it when the work also needs repo, CI, or infrastructure automation steps.
• notebooklm-management: Use it when the user also needs retrieval-oriented research workflows outside the deployment stack.