Chatqna Helm Deploy

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SPDX-License-Identifier: Apache-2.0
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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.

Environment setup (run first)

This skill operates on real ChatQnA source files, so the ChatQnA application must be present and commands must run from the app root. Do this before any Helm workflow, whether or not source is already in your workspace.

Run the bundled bootstrap. It searches for an existing ChatQnA checkout by walking up from the current directory and checking the enclosing git repo, then reuses it without re-cloning. Only when no checkout is found does it do a shallow, single-branch, sparse checkout of just sample-applications/chat-question-and-answer-core from main.

It prints the resolved app root on stdout:

# SKILL_DIR is this skill directory. In-repo it is:
# .github/skills/chatqna-helm-deploy
SKILL_DIR=".github/skills/chatqna-helm-deploy"
APP_ROOT="$(bash "$SKILL_DIR/scripts/chatqna-bootstrap.sh")"
cd "$APP_ROOT"

Every command below assumes the working directory is this APP_ROOT.

To use a fork/branch or a specific clone path, override these before running the bootstrap script:

• CHATQNA_REPO_URL
• CHATQNA_REPO_BRANCH
• CHATQNA_CLONE_DIR
• CHATQNA_FORCE_CLONE (set to 1 to force clone)

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.