Aiq Deploy

AIQ Deploy Skill

Overview

This skill owns setup, deployment, operational checks, troubleshooting, and shutdown of the NVIDIA AI-Q Blueprint server. It does not run deep research itself. After deployment is healthy, hand off the verified server URL to aiq-research.

The workflow stays explicit so deployment validation and handoff are repeatable across supported agent clients.

Version Compatibility: This skill targets NVIDIA AI-Q Blueprint version 2.1.0.

Skill version: X.Y.Z
Blueprint version: A.B.C

Compatible IF:
1. A == X  (Major versions MUST match)
2. B >= Y  (Minor version must be equal or greater)
3. C can be anything (Patch version does not affect compatibility)

• Skill 2.1.0 ↔ Blueprint 2.1.0 ✅
• Skill 2.1.0 ↔ Blueprint 2.2.0 ✅
• Skill 2.1.0 ↔ Blueprint 2.1.5 ✅
• Skill 2.1.0 ↔ Blueprint 3.0.0 ❌
• Skill 2.1.0 ↔ Blueprint 2.0.0 ❌

If the Blueprint version is not compatible, check for an updated skill, use a compatible Blueprint version, or proceed only when the user accepts the risk.

When to Use

Use this skill when the user asks to:

• Install or clone the NVIDIA AI-Q Blueprint repository.
• Deploy or run AI-Q locally or on a cluster (Docker Compose, Kubernetes/Helm, local process, CLI, or browser UI).
• Validate that the AI-Q backend is healthy and ready for aiq-research.
• Troubleshoot deployment issues: port conflicts, missing credentials, unhealthy services, config failures.
• Stop, restart, rebuild, or clean up AI-Q services.
• Hand off a verified AIQ_SERVER_URL to aiq-research.

Do not use this skill for deep research report generation — that belongs to aiq-research.

Prerequisites

• Access to clone or update https://github.com/NVIDIA-AI-Blueprints/aiq.
• Git available in the shell.
• One deployment runtime:
  • Docker Engine with Docker Compose v2 — default durable local deployment.
  • Python 3.11+ and uv — local process or CLI mode.
  • Node.js 20+ and npm — local browser UI development mode.
  • kubectl 1.28+, Helm 3.12+, and cluster access — Kubernetes/Helm mode.
• Network access to GitHub, NVIDIA-hosted model endpoints, and any selected search provider.
• Credentials stored outside chat. Hosted-model usage requires NVIDIA_API_KEY; web research requires at least one supported search provider key: TAVILY_API_KEY, SERPER_API_KEY, or EXA_API_KEY.
• System capacity for the selected runtime. Docker Compose mode starts the AI-Q backend and PostgreSQL by default; browser UI mode also uses frontend port 3000. Self-hosted model or RAG deployments may require GPU resources.

Windows host (PowerShell) note: Commands shown in bash syntax work in Git Bash or WSL. In PowerShell, replace test -f with Test-Path, cp with Copy-Item, and export VAR=... with $env:VAR = "...". Use curl.exe (not the curl alias to Invoke-WebRequest) for health checks.

Procedure

Step 1 — Locate or clone AI-Q

When to load: If no AI-Q checkout exists or the user asks to install/clone AI-Q, read references/locate-or-clone.md before cloning.

In an existing checkout, confirm the required files:

pwd
test -f pyproject.toml
test -f deploy/.env.example
test -d configs

Expected output: pwd prints the AI-Q repository path; the test commands exit with status 0 and produce no output.

PowerShell equivalent:

Get-Location
Test-Path pyproject.toml
Test-Path deploy/.env.example
Test-Path configs

Step 2 — Select the deployment mode

If the user asks to install, deploy, set up, or run AI-Q without naming a mode, ask:

How do you want to run AI-Q?

1. Skill backend - backend-only service for aiq-research w/o browser UI.
2. CLI - interactive terminal AI-Q.
3. UI - browser AI-Q app with backend and frontend.
4. Custom - choose an existing AI-Q config or review advanced customization docs before deployment.

Wait for the user's answer before starting services.

Do not ask this question when:

• The user already specified a mode (Docker Compose, Helm, UI, CLI, or Agent Skill backend).
• aiq-research routed here because a deep research request needs a backend. In that case, prefer Agent Skill backend and ask only for permission to start it if needed.

Step 3 — Prepare environment and secrets

When to load: Read references/env-and-secrets.md before changing deploy/.env.

if [ ! -f deploy/.env ]; then
  cp deploy/.env.example deploy/.env
  echo "created deploy/.env from deploy/.env.example"
fi

Expected output when the file is missing: created deploy/.env from deploy/.env.example Expected output when the file exists: no output; the existing file is preserved.

Before writing secrets, verify deploy/.env is git-ignored:

git check-ignore deploy/.env

Expected output: deploy/.env or a matching ignore rule. If it is not ignored, stop and fix the ignore rule before placing credentials in the file.

HARD RULES:

• Never print secret values. Check only whether required environment variables are set.
• Never overwrite deploy/.env when it already exists.
• Never ask the user to paste secret values into chat. Ask them to update deploy/.env directly.

Step 4 — Route to the selected deployment path

Match the user request, then read the referenced file before acting:

Step 5 — Validate and hand off

When to load: After startup, read references/validation.md and run the appropriate checks for the selected mode.

For the default local backend, verify health:

curl -sf http://localhost:8000/health

Expected output: a successful JSON health response or an empty successful response depending on the server build. If the command fails, read references/troubleshooting.md and diagnose before claiming the backend is ready.

aiq-research needs a reachable AI-Q server URL. If the backend is on the default port:

AIQ_SERVER_URL=http://localhost:8000

If the backend runs elsewhere:

export AIQ_SERVER_URL="http://localhost:<PORT>"

PowerShell:

$env:AIQ_SERVER_URL = "http://localhost:8000"

HARD RULE: Do not continue into deep research or deep research completion validation unless the user asks for it or confirms the post-deploy validation prompt. This skill's success criterion is a deployed and basically validated server, not report generation quality.

Examples

Example 1: Deploy a backend-only Skill server with Docker Compose

test -f deploy/.env || cp deploy/.env.example deploy/.env
git check-ignore deploy/.env
cd deploy/compose
BUILD_TARGET=release docker compose --env-file ../.env -f docker-compose.yaml config --quiet
BUILD_TARGET=release docker compose --env-file ../.env -f docker-compose.yaml up -d --build aiq-agent
curl -sf http://localhost:8000/health

Expected output:

deploy/.env
<docker compose starts aiq-agent and dependencies>
<health endpoint returns a successful response>

If Docker, ports, credentials, or health checks fail, read references/troubleshooting.md before retrying.

Example 2: Hand off a non-default backend URL to aiq-research

export AIQ_SERVER_URL="http://localhost:8100"
curl -sf "$AIQ_SERVER_URL/health"

Expected output: a successful health response. Then tell the user to keep AIQ_SERVER_URL set before invoking aiq-research.

Pitfalls

Backend port is already in use

Symptoms: Docker Compose fails to bind port 8000; curl -sf http://localhost:8000/health reaches an unexpected service or fails.

Causes: Another AI-Q backend or local dev server is running; PORT in deploy/.env conflicts.

Solutions:

1. Identify the process:

   lsof -nP -iTCP:8000 -sTCP:LISTEN
   

2. Either stop the conflicting process with the user's approval or set a different port in deploy/.env (e.g., PORT=8100).
3. Restart and verify:

   curl -sf http://localhost:8100/health
   

Required credentials are missing

Symptoms: Infrastructure starts, but model-backed chat or research requests fail. Logs mention unauthorized, forbidden, invalid key, or missing provider configuration.

Causes: NVIDIA_API_KEY is missing or empty; no supported search provider key is configured.

Solutions:

1. Check presence without printing values by following references/env-and-secrets.md.
2. Ask the user to update deploy/.env; do not ask them to paste secrets into chat.
3. Rerun references/validation.md after the user updates credentials.

Backend is healthy but not compatible with aiq-research

Symptoms: /health succeeds, but /chat or /v1/jobs/async/agents fails. aiq-research reports async agents unavailable.

Causes: The selected config is CLI-only or does not expose the web/API backend expected by the skill. BACKEND_CONFIG or CONFIG_FILE points at the wrong AI-Q config.

Solutions:

1. Read references/configs.md and confirm the selected config is API-enabled.
2. For the default Skill backend, use configs/config_web_default_llamaindex.yml.
3. Restart the backend and rerun references/validation.md.

Docker cleanup would remove useful state

Symptoms: Troubleshooting suggests docker compose down -v; the user may have local PostgreSQL job or checkpoint data they want to keep.

Causes: down -v removes Docker volumes. Rebuilds and restarts are often enough for config or image changes.

Solutions:

1. Prefer a normal restart from references/shutdown.md.
2. Ask for explicit approval before running volume deletion.
3. After cleanup, rerun deployment and validation from the selected route.

FRAG not ready

HARD RULE: Do not claim FRAG is ready unless both RAG_SERVER_URL and RAG_INGEST_URL are configured and reachable. Read references/frag.md for setup details.

Verification

Run these checks after the selected deployment path starts:

1. Health endpoint (default port 8000):

   curl -sf http://localhost:8000/health
   

   Expected: successful JSON or empty 200 response.

2. Non-default port (if applicable):

   curl -sf "$AIQ_SERVER_URL/health"
   

   Expected: successful health response.

3. Git ignore check (should already be done before secrets):

   git check-ignore deploy/.env
   

   Expected: deploy/.env or matching ignore rule.

4. Repository file check (in existing checkout):

   test -f pyproject.toml && test -f deploy/.env.example && test -d configs && echo "OK"
   

   Expected: OK

5. Docker Compose config validation (Docker mode only):

   cd deploy/compose
   BUILD_TARGET=release docker compose --env-file ../.env -f docker-compose.yaml config --quiet
   

   Expected: no output (valid config).

If any check fails, read references/troubleshooting.md and diagnose before reporting success.

Related Skills

• aiq-research — Consumes the verified AIQ_SERVER_URL produced by this skill to run deep research workflows. Hand off the URL only after validation passes.

References