AIQ Deploy Skill

Purpose

Use this skill to get a local or self-hosted NVIDIA AI-Q Blueprint server running and verified for use by aiq-research.

This skill owns setup, deployment, operational checks, troubleshooting, and shutdown. 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.

Prerequisites

Users need:

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 for the default durable local deployment.
- Python 3.11+ and uv for local process or CLI mode.
- Node.js 20+ and npm for local browser UI development mode.
- kubectl 1.28+, Helm 3.12+, and access to a Kubernetes cluster for 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 such as 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.

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

bash

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.

Instructions

Locate or clone the AI-Q repository.
Confirm the expected repository files exist.
Select the deployment mode.
Prepare deploy/.env without overwriting user secrets.
Check runtime prerequisites for the selected path.
Start the selected deployment.
Run basic validation.
Report the verified AIQ_SERVER_URL for aiq-research.
Ask whether to run optional deep research completion validation.

Step 1 - Locate or clone AI-Q

If no AI-Q checkout exists, read references/locate-or-clone.md before cloning. In an existing checkout, confirm the required files:

bash

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 no output.

Step 2 - Select the deployment mode

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

text

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, such as Docker Compose, Helm, UI, CLI, or Agent Skill backend. Do not ask the full mode question when 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

Read references/env-and-secrets.md before changing deploy/.env.

bash

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 already exists: no output, and the existing file is preserved.

Never print secret values. If credentials are missing, ask the user to update deploy/.env; do not ask them to paste secret values into chat.

Step 4 - Route to the selected deployment path

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

User Intent	Reference
No AI-Q checkout exists, install AIQ, clone AIQ, locate repo	`references/locate-or-clone.md`
Configure environment, check API keys, inspect `.env`	`references/env-and-secrets.md`
Choose an AI-Q workflow config, understand config files, set `BACKEND_CONFIG` or `CONFIG_FILE`	`references/configs.md`
Backend-only local server for `aiq-research`, AIQ as an Agent Skill	`references/skill-backend.md`
Terminal assistant, CLI-only run, no web UI	`references/terminal-cli.md`
Quick local development run, start UI/backend without containers	`references/local-web.md`
Default durable local deployment, Docker Compose, containers, PostgreSQL	`references/docker-compose.md`
Kubernetes, Helm, cluster deployment	`references/kubernetes-helm.md`
Foundational RAG / FRAG integration	`references/frag.md`
Basic health checks, shallow smoke checks, handoff to `aiq-research`	`references/validation.md`
Optional deep research completion validation	`references/end-to-end-validation.md`
Logs, unhealthy services, port conflicts, config failures	`references/troubleshooting.md`
Stop services, restart, rebuild, safe cleanup	`references/shutdown.md`

Step 5 - Validate and hand off

After startup, read references/validation.md and run the appropriate checks for the selected mode. For the default local backend, verify health:

bash

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, no extra configuration is needed:

bash

AIQ_SERVER_URL=http://localhost:8000

If the backend runs elsewhere, tell the user to set:

bash

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

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.

Version Compatibility

IMPORTANT: This skill is designed for NVIDIA AI-Q Blueprint version 2.1.0.

Semantic Versioning Compatibility Rules:

text

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)

Examples:

Skill version 2.1.0 is compatible with Blueprint version 2.1.0.
Skill version 2.1.0 is compatible with Blueprint version 2.2.0.
Skill version 2.1.0 is compatible with Blueprint version 2.1.5.
Skill version 2.1.0 is not compatible with Blueprint version 3.0.0.
Skill version 2.1.0 is not compatible with Blueprint version 2.0.0.

If your Blueprint version is not compatible:

Check for an updated skill version matching your Blueprint version.
Use a Blueprint version compatible with this skill.
Proceed with caution only when the user accepts the compatibility risk; deployment commands or config names may have changed.

Security Best Practices

Never print secret values. Check only whether required environment variables are set.
Store credentials in deploy/.env or environment variables, not in chat transcripts, shell history, committed files, or example commands.
Do not overwrite deploy/.env when it already exists.
Ask before destructive cleanup such as deleting Docker volumes with down -v.
Do not claim FRAG is ready unless both RAG_SERVER_URL and RAG_INGEST_URL are configured and reachable.
Run verification commands yourself when possible.

Limitations

This skill prepares and validates AI-Q infrastructure; it does not judge deep research report quality.
It cannot provide or inspect secret values. Users must configure credentials outside chat.
Helm, FRAG, custom config, and self-hosted model paths depend on infrastructure the user controls.
Destructive cleanup, such as deleting Docker volumes, requires explicit user approval.

Examples

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

bash

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:

text

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

bash

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.

References

Topic	Documentation
Locate or clone AI-Q	`references/locate-or-clone.md`
Environment and secrets	`references/env-and-secrets.md`
Workflow configs	`references/configs.md`
Agent Skill backend	`references/skill-backend.md`
CLI deployment	`references/terminal-cli.md`
Local web deployment	`references/local-web.md`
Docker Compose deployment	`references/docker-compose.md`
Kubernetes and Helm deployment	`references/kubernetes-helm.md`
FRAG integration	`references/frag.md`
Basic validation	`references/validation.md`
End-to-end validation	`references/end-to-end-validation.md`
Troubleshooting	`references/troubleshooting.md`
Shutdown and cleanup	`references/shutdown.md`

Common Issues

Issue: 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 development server is already running.
PORT in deploy/.env conflicts with an existing process.

Solutions:

Identify the process:

bash

lsof -nP -iTCP:8000 -sTCP:LISTEN

Either stop the conflicting process with the user's approval or set a different port in deploy/.env, such as PORT=8100.

Restart the selected deployment path and verify:

bash

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

Issue: 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 for web research.

Solutions:

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

Issue: Backend is healthy but not compatible with aiq-research

Symptoms:

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

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

Issue: 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:

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