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open-edge-platform/edge-ai-libraries/sample-applications/chat-question-and-answer-core/.github/skills/chatqna-helm-deploy/SKILL.md

chatqna-helm-deploy

Deploy Chat Question-and-Answer Core to Kubernetes using Helm (OpenVINO CPU, OpenVINO GPU, or Ollama), including values.yaml configuration, helm install/upgrade, deployment verification, uninstall, and translation from Docker Compose setup_env.sh variables into Helm override values. Use this skill when the user says "deploy chatqna core to kubernetes", "helm install chatqna-core", "configure values.yaml", "convert compose config to helm", or "translate setup_env.sh to chart values".

Source repository stars
156
Declared platforms
0
Static risk flags
1
Last source update
2026-08-24
Source checked
2026-08-25

Decision brief

What it does: where it fits

Deploy the Chat Question and Answer Core sample application Helm chart at sample-applications/chat-question-and-answer-core/chart/ to Kubernetes using Helm. The chart's dependencies are chatqna-core and chatqna-ui, which are built from the same source code as the Docker Compose…

Best for

  • "Deploy chatqna core to Kubernetes"
  • "Helm install chatqna-core"
  • "Configure values.yaml for chatqna core"

Not for

  • Tasks that require unconfirmed production actions or broad system permissions.
  • Environments where the pinned source and install steps cannot be inspected.

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexNot declaredNo explicit evidencePortability before use
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/open-edge-platform/edge-ai-libraries --skill "sample-applications/chat-question-and-answer-core/.github/skills/chatqna-helm-deploy"
Safe inspection promptEditorial

Inspect the Agent Skill "chatqna-helm-deploy" from https://github.com/open-edge-platform/edge-ai-libraries/blob/4ee15c40c9ea483d105b3a5f4d51d5bde2879a1b/sample-applications/chat-question-and-answer-core/.github/skills/chatqna-helm-deploy/SKILL.md at commit 4ee15c40c9ea483d105b3a5f4d51d5bde2879a1b. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    Deployment Workflow

    Run from sample-applications/chat-question-and-answer-core.

    OpenVINO: values.yaml + values-openvino.yaml + values-override.yamlOllama: values.yaml + values-ollama.yaml + values-override.yamlRun from sample-applications/chat-question-and-answer-core.
  2. 02

    Prerequisites

    1. Confirm a reachable Kubernetes cluster is available and kubectl is configured to access it.

    Confirm a reachable Kubernetes cluster is available and kubectl is configured to access it.For GPU, discover resource keys before writing values:1. Confirm a reachable Kubernetes cluster is available and kubectl is configured to access it.
  3. 03

    What This Skill Produces

    A running ChatQnA Core Helm release in a target namespace for one runtime:

    A running ChatQnA Core Helm release in a target namespace for one runtime:OpenVINO CPUOpenVINO GPU
  4. 04

    When to Use

    "Deploy chatqna core to Kubernetes"

    "Deploy chatqna core to Kubernetes""Helm install chatqna-core""Configure values.yaml for chatqna core"
  5. 05

    Inputs To Confirm

    Before running commands, confirm or infer these values:

    Runtime: openvino or ollamaDevice target: cpu or gpu (GPU valid only for OpenVINO)Namespace and release name (default release: chatqna-core)

Permission review

Static risk signals and limitations

Network access

medium · line 186

The documentation includes network, browsing, or remote request actions.

curl -sS -w "\nHTTP_STATUS:%{http_code}\n" "http://${chatqna_hostip}:${chatqna_port}/v1/chatqna/health"

Network access

medium · line 193

The documentation includes network, browsing, or remote request actions.

echo "http://${chatqna_hostip}:${chatqna_port}"

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score96/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars156SourceRepository attention, not individual Skill quality
Compatibility0 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
open-edge-platform/edge-ai-libraries
Skill path
sample-applications/chat-question-and-answer-core/.github/skills/chatqna-helm-deploy/SKILL.md
Commit
4ee15c40c9ea483d105b3a5f4d51d5bde2879a1b
License
Apache-2.0
Collected
2026-08-25
Default branch
main
View the original SKILL.md

ChatQnA Helm Deploy

Deploy the Chat Question and Answer Core sample application Helm chart at sample-applications/chat-question-and-answer-core/chart/ to Kubernetes using Helm. The chart's dependencies are chatqna-core and chatqna-ui, which are built from the same source code as the Docker Compose deployment. Also it includes nginx as a reverse proxy for the backend and UI.

Codebase root: sample-applications/chat-question-and-answer-core/

Prerequisites

  1. Confirm a reachable Kubernetes cluster is available and kubectl is configured to access it.

      kubectl get nodes
    
  2. For GPU, discover resource keys before writing values:

      kubectl get nodes -o json | jq -r '.items[] | "\(.metadata.name):\n" + (.status.allocatable | to_entries | map(select(.key | test("gpu|npu|vpu|accel";"i"))) | map("  \(.key): \(.value)") | join("\n"))'
    

    Common Intel keys are gpu.intel.com/i915, gpu.intel.com/xe

What This Skill Produces

  • A running ChatQnA Core Helm release in a target namespace for one runtime:
    • OpenVINO CPU
    • OpenVINO GPU
    • Ollama
  • A generated override file (values-override.yaml) that translates Docker Compose and setup_env.sh style inputs to Helm values keys.
  • A verified deployment state using pods, services, and health endpoint checks.
  • A concise deployment report containing:
    • runtime selected and GPU mode
    • chart source (local path or OCI chart)
    • values files used and major override keys
    • access URL and API docs URL
    • warnings (missing token, GPU key, model constraints)

When to Use

  • "Deploy chatqna core to Kubernetes"
  • "Helm install chatqna-core"
  • "Configure values.yaml for chatqna core"
  • "Translate docker compose setup_env.sh into helm values"
  • "Deploy OpenVINO GPU profile with Helm"
  • "Deploy Ollama with chart values"

Inputs To Confirm

Before running commands, confirm or infer these values:

  1. Runtime: openvino or ollama
  2. Device target: cpu or gpu (GPU valid only for OpenVINO)
  3. Namespace and release name (default release: chatqna-core)
  4. Chart source:
    • local chart path (./chart), or
    • OCI chart (oci://registry-1.docker.io/intel/chat-question-and-answer-core)
  5. Image source and tags:
    • prebuilt registry tags, or
    • custom/private registry and tags
  6. Model settings (EMBEDDING_MODEL, LLM_MODEL, optional RERANKER_MODEL)
  7. Optional Hugging Face token (HUGGINGFACEHUB_API_TOKEN) for OpenVINO
  8. Optional proxy values (http_proxy, https_proxy, no_proxy)

If runtime/device values are missing, default to openvino + cpu.

If prebuilt images are used and tags are not specified by the user, default to the tags in chart/values.yaml.

Use Helm and kubectl commands for deployment actions in this skill.

Decision Logic

  • If runtime is ollama:
    • select -f values.yaml -f values-ollama.yaml
    • force CPU-only devices
  • If runtime is openvino and device is gpu:
    • select -f values.yaml -f values-openvino.yaml
    • set gpu.enabled=true
    • require gpu.key from cluster labels
  • If runtime is openvino and device is cpu:
    • select -f values.yaml -f values-openvino.yaml
    • set gpu.enabled=false
  • If requested values conflict with chart validation (for example GPU model device with gpu.enabled=false), correct values before install.

Compose and setup_env.sh Translation

Use the reference mapping in ./references/compose-setupenv-to-helm-mapping.md if the user asks to map Compose or setup_env.sh inputs to Helm values.

Deployment Workflow

Run from sample-applications/chat-question-and-answer-core.

1. Preflight

kubectl version --client
helm version
kubectl config current-context

If using local source chart:

cd chart
helm dependency build

If using OCI chart:

helm pull oci://registry-1.docker.io/intel/chat-question-and-answer-core --version <version>
tar -xvf chat-question-and-answer-core-<version>.tgz
cd chat-question-and-answer-core
helm dependency build

Ensure namespace exists:

kubectl create namespace <namespace> --dry-run=client -o yaml | kubectl apply -f -

2. Build values override file

Create or update values-override.yaml by translating user intent or Compose/setup_env style inputs using the reference mapping. Do not commit filled secrets or tokens.

If running behind a proxy, include these keys in values-override.yaml using the values from your current system environment:

global:
	http_proxy: "${http_proxy}"
	https_proxy: "${https_proxy}"
	no_proxy: "${no_proxy}"

Select base files by runtime:

  • OpenVINO: values.yaml + values-openvino.yaml + values-override.yaml
  • Ollama: values.yaml + values-ollama.yaml + values-override.yaml

3. Validate rendered manifests

helm template chatqna-core \
	-f values.yaml \
	-f values-<runtime>.yaml \
	-f values-override.yaml \
	.

4. Install or upgrade release

helm upgrade --install chatqna-core \
	-f values.yaml \
	-f values-<runtime>.yaml \
	-f values-override.yaml \
	. \
	--namespace <namespace>

5. Verify deployment

kubectl get pods -n <namespace>
kubectl get services -n <namespace>
kubectl get events -n <namespace> --sort-by=.lastTimestamp | tail -n 30
kubectl rollout status deploy/chatqna-core -n <namespace>
kubectl rollout status deploy/chatqna-core-nginx -n <namespace>

Health endpoint evidence:

chatqna_hostip=$(kubectl get pods -l app=chatqna-core-nginx -n <namespace> -o jsonpath='{.items[0].status.hostIP}')
chatqna_port=$(kubectl get service chatqna-core-nginx -n <namespace> -o jsonpath='{.spec.ports[0].nodePort}')
curl -sS -w "\nHTTP_STATUS:%{http_code}\n" "http://${chatqna_hostip}:${chatqna_port}/v1/chatqna/health"

6. Access and teardown

# UI
echo "http://${chatqna_hostip}:${chatqna_port}"

# API docs
echo "http://${chatqna_hostip}:${chatqna_port}/v1/chatqna/docs"

# Uninstall
helm uninstall chatqna-core -n <namespace>

Failure Handling

  • Helm template validation fails:
    • report exact key causing failure and propose corrected key/value.
  • GPU requested but gpu.key missing:
    • instruct user to run kubectl describe node and provide device plugin key, then re-run with gpu.enabled=true.
  • Pods not ready:
    • collect kubectl describe pod and kubectl logs for failing pods.
  • Health check non-200:
    • inspect chatqna-core logs for model download/config issues.
    • note first startup can take longer due to model pull/conversion.
  • PVC stuck:
    • list and optionally delete stuck PVC only when explicitly requested.
  • Need larger storage:
    • increase PVC size in values-override.yaml and re-run helm upgrade.

Completion Criteria

  1. Runtime-specific install command is executed with correct values files.
  2. Compose/setup_env inputs (if provided) are translated into a concrete values-override.yaml.
  3. Pods/services are healthy in the target namespace.
  4. Health endpoint returns HTTP_STATUS:200.
  5. User receives UI URL, API docs URL, release/namespace, and uninstall command.
  6. Response includes raw verification evidence (kubectl get, rollout status, health check output).

Frequently asked questions

What to verify before installation and use

What does the chatqna-helm-deploy source document cover?

Deploy the Chat Question and Answer Core sample application Helm chart at sample-applications/chat-question-and-answer-core/chart/ to Kubernetes using Helm. The chart's dependencies are chatqna-core and chatqna-ui, which are built from the same source code as the Docker Compose…

How do I install chatqna-helm-deploy?

The source record exposes this install command: npx skills add https://github.com/open-edge-platform/edge-ai-libraries --skill "sample-applications/chat-question-and-answer-core/.github/skills/chatqna-helm-deploy". Inspect the command and pinned source before running it.

Which permission-related actions were detected?

Static rules flagged network in the source; the page lists the matching lines and excerpts.

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