aiq-deploy

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:

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

1. Locate or clone the AI-Q repository.
2. Confirm the expected repository files exist.
3. Select the deployment mode.
4. Prepare deploy/.env without overwriting user secrets.
5. Check runtime prerequisites for the selected path.
6. Start the selected deployment.
7. Run basic validation.
8. Report the verified AIQ_SERVER_URL for aiq-research.
9. 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:

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:

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.

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:

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:

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:

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

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:

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:

1. Check for an updated skill version matching your Blueprint version.
2. Use a Blueprint version compatible with this skill.
3. 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

Expected output:

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

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

References

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:

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, such as PORT=8100.
3. Restart the selected deployment path and verify:

   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:

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.

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:

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.

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:

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.