cu-sdk-setup

name	cu-sdk-setup
description	Guide SDK users through setting up their JavaScript / Node.js environment for Azure AI Content Understanding. Use this skill when users need help installing the SDK, configuring Azure resources, deploying required models, setting environment variables, or running samples.

SDK User Environment Setup for Azure AI Content Understanding (JavaScript)

Set up your JavaScript / Node.js environment to use the Azure AI Content Understanding SDK and run samples.

[COPILOT INTERACTION MODEL]: This skill is designed to be interactive. At each step marked with [ASK USER], pause execution and prompt the user for input or confirmation before proceeding. Do NOT silently skip these prompts. Use the ask_questions tool when available.

Prerequisites

Before starting, ensure you have:

• Node.js 20 or later installed
• An Azure subscription (create one for free)
• A Microsoft Foundry resource in a supported region

[COPILOT] Probe Node.js runtime first (before asking): Do not take the user's word for it — run these checks, then report. This prevents silent failures later in npm install.

# POSIX / WSL / macOS
node --version 2>/dev/null
npm --version 2>/dev/null

# Windows PowerShell
node --version
npm --version

Decision table:

Finding	Action
node v20+ and npm present	✓ Good to go. Proceed to Step 1.
Node missing or < v20	Report the finding, then go to the [ASK USER] Node install choice block below.
npm missing (very rare — should ship with Node)	Reinstall Node.js LTS.

[ASK USER] Node install choice (only when probe fails): Ask the user: "Node.js is missing or older than v20. How would you like to proceed?"

• Option A: Install it for me — Agent runs the platform-appropriate install command (see below), verifies, and continues.
• Option B: I'll install it myself — Agent prints the install command for the user's platform and stops. User runs it, re-opens the terminal, and tells the agent to resume.

Default install commands (Option A):

• macOS → brew install node (requires Homebrew; if not installed, fall back to Option B)
• Debian / Ubuntu / WSL → curl -fsSL https://deb.nodesource.com/setup_lts.x | sudo -E bash - && sudo apt-get install -y nodejs
• Windows → winget install OpenJS.NodeJS.LTS (run in an elevated PowerShell if needed)
• Cross-platform via nvm → curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash && nvm install --lts

The companion setup_user_env.sh / setup_user_env.ps1 scripts already implement this probe + install flow automatically. Prefer running them rather than hand-rolling these steps.

Report the detected Node version back to the user in one sentence before the [ASK USER] block below.

[ASK USER] Prerequisites Check: After the probe above, confirm the remaining items:

1. "Do you already have a Microsoft Foundry resource set up in Azure?" — If no, jump to Step 4 (Azure Resource Setup) first, then return here.
2. "Have you already deployed the required AI models (GPT-4.1, GPT-4.1-mini, text-embedding-3-large) in Microsoft Foundry?" — If no, include Step 4.3 and Step 5 in the workflow.

Package Directory

sdk/contentunderstanding/ai-content-understanding

Workflow

Step 1: Navigate to Package Directory

cd sdk/contentunderstanding/ai-content-understanding

Step 2: Install the SDK

[ASK USER] Installation mode: Ask the user: "How would you like to install the SDK?"

• Option A: npm install (recommended) — Installs the latest published version from the npm registry. Best for running samples or building Content Understanding-based applications.
• Option B: Local build + tarball (for SDK contribution) — Builds the package from local source, packs a tarball, and installs it into the samples directory. Use this when contributing to the SDK or testing unpublished changes.

The companion script setup_user_env.sh / setup_user_env.ps1 handles both paths automatically: it first tries npm install from the registry and falls back to a local pnpm build + tarball if the package is not yet published. It also writes .env and copies it into the samples directory.

Option A: npm install (recommended, in your own project):

npm install @azure/ai-content-understanding @azure/identity

Option B: Local build + tarball (for SDK contribution):

# From the package root
.github/skills/cu-sdk-setup/scripts/setup_user_env.sh --local

[COPILOT] Repeated-run behavior: On repeated runs, if @azure/ai-content-understanding is already installed under samples/v1/javascript/node_modules/, the setup script may skip the install step. Only rerun the install commands when the package is missing or you want to force a refresh.

[ASK USER] Installation check: After running the install commands, ask: "Did the installation complete without errors?" If the user reports errors (permissions, missing pnpm for Option B, build failure), help troubleshoot before continuing.

Step 3: Configure Environment Variables

3.1 Create .env (with safety check)

Important: This step copies the template without overwriting any existing .env file.

if [ -f ".env" ]; then
    echo "WARNING: .env file already exists - NOT overwriting"
    echo "If you want to start fresh, manually delete .env first: rm .env"
else
    cp sample.env .env
    echo "Created .env from sample.env"
    echo "Please edit .env and configure the required variables (see Step 3.2)"
fi

For Windows PowerShell:

if (Test-Path ".env") {
    Write-Host "WARNING: .env file already exists - NOT overwriting"
    Write-Host "If you want to start fresh, manually delete .env first: Remove-Item .env"
} else {
    Copy-Item sample.env .env
    Write-Host "Created .env from sample.env"
    Write-Host "Please edit .env and configure the required variables (see Step 3.2)"
}

[ASK USER] Existing .env: If a .env file already exists, ask: "A .env file already exists. Would you like to keep the existing one, or start fresh by deleting it and copying from sample.env?"

3.2 Configure Required Variables

[ASK USER] Authentication method: Ask the user: "How would you like to authenticate with Azure?"

• Option A: API Key — You'll need your CONTENTUNDERSTANDING_KEY from the Azure Portal.
• Option B: DefaultAzureCredential (recommended) — Uses az login or managed identity. No API key needed.

Based on their choice, guide accordingly below.

[ASK USER] Provide endpoint: Ask the user: "Please provide your Microsoft Foundry endpoint URL."

• It should look like: https://<your-resource-name>.services.ai.azure.com/
• Validate: it should NOT include api-version or other query parameters.
• If the user doesn't know where to find it: direct them to Azure Portal → Their Foundry resource → Keys and Endpoint.

[ASK USER] Provide API key (if Option A): If the user chose API Key authentication, ask: "Please provide your API key (CONTENTUNDERSTANDING_KEY)."

• Found at: Azure Portal → Your Foundry resource → Keys and Endpoint → Key1 or Key2.

If the user chose DefaultAzureCredential, remind them: "Make sure you've run az login to authenticate."

Open .env in your editor and set the following required variables:

Variable	Description	How to Get It
CONTENTUNDERSTANDING_ENDPOINT	Your Microsoft Foundry endpoint URL	Azure Portal → Your Foundry resource → Keys and Endpoint
CONTENTUNDERSTANDING_KEY	API key (optional if using DefaultAzureCredential)	Azure Portal → Your Foundry resource → Keys and Endpoint → Key1 or Key2

For running updateDefaults.js (one-time model configuration):

[COPILOT] Probe existing model defaults on the Foundry resource: Before asking the user for deployment names, probe what the resource already has configured. This avoids redundant prompting when the resource is already wired up. Use a raw HTTP call (curl + Azure CLI access token) so the probe works even when the SDK is not yet installed in the samples directory.

EP="${CONTENTUNDERSTANDING_ENDPOINT%/}"
APIVER="2025-11-01"
if [ -n "${CONTENTUNDERSTANDING_KEY:-}" ]; then
    RESP=$(curl -sS -w "\n%{http_code}" -H "Ocp-Apim-Subscription-Key: $CONTENTUNDERSTANDING_KEY" \
        "$EP/contentunderstanding/defaults?api-version=$APIVER")
else
    TOKEN=$(az account get-access-token --resource https://cognitiveservices.azure.com --query accessToken -o tsv 2>/dev/null)
    RESP=$(curl -sS -w "\n%{http_code}" -H "Authorization: Bearer $TOKEN" \
        "$EP/contentunderstanding/defaults?api-version=$APIVER")
fi
CODE=$(printf '%s' "$RESP" | tail -n1)
BODY=$(printf '%s' "$RESP" | sed '$d')
# Parse with node (or jq); branch on $CODE first (200/401/403/other)

Branch on the HTTP code + parsed body:

Code / state	Meaning	Action
200 and all 3 keys (gpt-4.1, gpt-4.1-mini, text-embedding-3-large) present	ALL_SET — defaults already mapped	Show detected values; ask "Detected existing defaults: gpt-4.1=<A>, gpt-4.1-mini=<B>, text-embedding-3-large=<C>. Use these? (Y/n)". On Y, prefill the 3 env vars and skip Step 5 (defaults already configured). On n, fall through to per-model prompts.
200 and some but not all keys present	PARTIAL	Prefill the ones that are set. Prompt for the missing ones. After Step 3 completes, run Step 5 to fill the gaps.
200 and no model defaults	NONE	Fall through to per-model prompts. Step 5 will configure them.
401 or 403	AUTH_ERROR	Print: "Probe unavailable (auth failed). If you're using DefaultAzureCredential, run az login and ensure the Cognitive Services User role is assigned. Continuing with manual entry." Fall through.
other / connection failure	Unexpected	Print "Probe failed (HTTP <code>). Continuing with manual entry." Fall through.

These labels describe the Step 4 defaults-probe result only. They are not the overall setup_user_env script process exit codes.

The companion setup_user_env.sh / setup_user_env.ps1 script implements this probe automatically and prefills the prompts.

[ASK USER] Model deployment names (only when probe did not yield all values): For each model not already prefilled from the probe, ask with a sensible default:

• "What is your GPT-4.1 deployment name?" (default: gpt-4.1)
• "What is your GPT-4.1-mini deployment name?" (default: gpt-4.1-mini)
• "What is your text-embedding-3-large deployment name?" (default: text-embedding-3-large)

If the user prefers to configure these later, let them know they can run updateDefaults.js (Step 5) anytime before using prebuilt analyzers.

Variable	Description	How to Get It
GPT_4_1_DEPLOYMENT	Your GPT-4.1 deployment name	Microsoft Foundry → Deployments → Your GPT-4.1 deployment name
GPT_4_1_MINI_DEPLOYMENT	Your GPT-4.1-mini deployment name	Microsoft Foundry → Deployments → Your GPT-4.1-mini deployment name
TEXT_EMBEDDING_3_LARGE_DEPLOYMENT	Your text-embedding-3-large deployment name	Microsoft Foundry → Deployments → Your embedding deployment name

3.3 Copy .env into the Samples Directory

Samples use dotenv/config to load environment variables from the current working directory. Copy your .env into the samples folder so node can load it when running samples:

cp .env samples/v1/javascript/.env

Note: The setup_user_env.sh script does this automatically. If you edit .env later, re-run the script with --verify-only to recheck, or manually re-copy.

3.4 Validate Your Configuration

[ASK USER] Validate configuration: After the user has provided all values, summarize the configuration (mask any API key) and ask them to confirm:

Here's your configuration:
  CONTENTUNDERSTANDING_ENDPOINT = <value>
  Authentication: API Key / DefaultAzureCredential
  GPT_4_1_DEPLOYMENT = <value>
  GPT_4_1_MINI_DEPLOYMENT = <value>
  TEXT_EMBEDDING_3_LARGE_DEPLOYMENT = <value>

Does this look correct? (Yes / No — let me fix something)

Only write to .env after the user confirms.

Example .env configuration:

# Required for all samples
CONTENTUNDERSTANDING_ENDPOINT=https://my-foundry-resource.services.ai.azure.com/

# Optional: API key (if not set, DefaultAzureCredential is used)
CONTENTUNDERSTANDING_KEY=

# Required for updateDefaults.js (model configuration)
GPT_4_1_DEPLOYMENT=gpt-4.1
GPT_4_1_MINI_DEPLOYMENT=gpt-4.1-mini
TEXT_EMBEDDING_3_LARGE_DEPLOYMENT=text-embedding-3-large

Step 4: Azure Resource Setup (if not done)

[NOTE]: Only guide the user through this step if they indicated during the prerequisites check that they do NOT yet have a Microsoft Foundry resource. Otherwise, skip to Step 5.

If you haven't set up your Microsoft Foundry resource yet:

4.1 Create Microsoft Foundry Resource

1. Go to Azure Portal
2. Create a Microsoft Foundry resource in a supported region
3. Navigate to Resource Management > Keys and Endpoint
4. Copy the Endpoint URL and optionally a Key

[ASK USER] Resource created: After guiding the user to create the resource, ask: "Have you created the Microsoft Foundry resource? Please share the endpoint URL so we can continue with configuration."

4.2 Grant Cognitive Services User Role

This role is required even if you own the resource:

1. In your Foundry resource, go to Access Control (IAM)
2. Click Add > Add role assignment
3. Select Cognitive Services User role
4. Assign it to yourself

[ASK USER] Role assigned: Ask: "Have you assigned the Cognitive Services User role to yourself? This is required even if you own the resource."

4.3 Deploy Required Models

Analyzer Type	Required Models
prebuilt-documentSearch, prebuilt-imageSearch, prebuilt-audioSearch, prebuilt-videoSearch	gpt-4.1-mini, text-embedding-3-large
Other prebuilt analyzers (invoice, receipt, etc.)	gpt-4.1, text-embedding-3-large

To deploy a model:

1. In Microsoft Foundry → Deployments → Deploy model → Deploy base model
2. Search and deploy: gpt-4.1, gpt-4.1-mini, text-embedding-3-large
3. Note deployment names (recommendation: use the model name as the deployment name)

[ASK USER] Models deployed: Ask: "Have you deployed the required models? Please provide the deployment names you used for each:"

• GPT-4.1 deployment name
• GPT-4.1-mini deployment name
• text-embedding-3-large deployment name

Use these names to populate the .env file.

Step 5: Configure Model Defaults (One-Time Setup)

[COPILOT] Skip condition: If the Step 3.2 probe returned ALL_SET and the user accepted the detected values, defaults are already configured on the Foundry resource — skip this step and tell the user "Your Foundry resource already has model defaults configured; skipping Step 5." Otherwise continue below.

[ASK USER] Run model defaults?: Ask: "Would you like to run updateDefaults.js now to configure model defaults? This is a one-time setup per Microsoft Foundry resource. (Yes / Skip for now)"

• If yes, ensure deployment name env vars are set, then run the script.
• If no, let them know they'll need to run it before using prebuilt analyzers.

Run the configuration script to map deployed models to prebuilt analyzers:

cd samples/v1/javascript
node updateDefaults.js

This is a one-time setup per Microsoft Foundry resource.

Step 6: Run Samples

[ASK USER] Which samples?: Ask: "Which sample would you like to run first?" with options:

• analyzeBinary.js — Analyze a local PDF (quickest; completes in under a minute)
• analyzeUrl.js — Full demo: document + video + audio + image from URLs (runs several analyses; takes a few minutes, please be patient)
• analyzeInvoice.js — Extract invoice fields
• Other — Let me see the full list
• Skip — I'll run samples on my own later

If the user picks "Other", list available samples from the samples/v1/javascript/ directory.

[COPILOT] Timing note (do not parrot verbatim to user): analyzeUrl.js runs multiple sequential LROs (document + video + audio + image, with multiple content-range variants). Video/audio chapter generation is slow on the service side, so total runtime can be on the order of 15+ minutes today. Do not interpret quiet periods (no stdout for several minutes during a video/audio LRO) as a hang. Only consider killing if there is no new stdout for 5+ minutes AND no active HTTP traffic. When talking to the user, prefer phrasing like "takes a few minutes" or "please be patient" rather than citing exact large minute counts.

# From the samples directory (so dotenv/config picks up the local .env)
cd samples/v1/javascript

# Run a sample
node analyzeUrl.js
node analyzeBinary.js

Or use the sample-run helper script (sources .env automatically and supports running from anywhere):

.github/skills/cu-sdk-sample-run/scripts/run_sample.sh analyzeUrl

[ASK USER] Sample result: After running a sample, ask: "Did the sample run successfully? Would you like to run another sample or are you all set?"

Automated Setup Script (Linux/macOS/WSL)

Run the interactive setup script that handles all steps automatically:

# From the package directory
cd sdk/contentunderstanding/ai-content-understanding
.github/skills/cu-sdk-setup/scripts/setup_user_env.sh

The script will:

1. Probe and (optionally) install Node.js (>= 20).
2. Detect existing .env and ask before overwriting.
3. Probe the Foundry resource for existing model defaults and prefill prompts.
4. Collect endpoint, auth method, and model deployment names.
5. Write .env (gitignored) at the package root, preserving any existing keys.
6. Install the SDK in samples/v1/javascript/ (npm registry, with local pnpm build + tarball fallback).
7. Copy .env into the samples directory so dotenv/config can load it.
8. Run a 5-step verification against the live endpoint.

Common flags:

• --verify-only — Skip install/config; just run the 5 checks.
• --non-interactive — No prompts; use existing .env / env vars / overrides.
• --local — Force local build + tarball (skip npm registry).
• --endpoint URL / --api-key KEY — Override prompts.

Automated Setup Script (Windows)

cd sdk/contentunderstanding/ai-content-understanding
.\.github\skills\cu-sdk-setup\scripts\setup_user_env.ps1

Same flags as the bash version (using PowerShell syntax: -VerifyOnly, -NonInteractive, -Local, -Endpoint, -ApiKey).

Manual Quick Setup

If you prefer to run steps manually:

cd sdk/contentunderstanding/ai-content-understanding

# Copy sample.env if .env doesn't exist
if [ ! -f ".env" ]; then
    cp sample.env .env
    echo "Created .env - Please edit and configure required variables"
else
    echo "WARNING: .env already exists - skipping copy"
fi

# Install SDK in samples directory (npm registry path)
( cd samples/v1/javascript && npm install )

# Copy .env into samples so dotenv/config can load it
cp .env samples/v1/javascript/.env

Troubleshooting

Error	Solution
node: command not found	Install Node.js 20+ from nodejs.org or via winget install OpenJS.NodeJS.LTS / brew install node.
Cannot find module '@azure/ai-content-understanding'	Run setup_user_env.sh to install (with automatic local-build fallback if not yet on npm).
Missing environment variables / CONTENTUNDERSTANDING_ENDPOINT	Ensure .env exists in samples/v1/javascript/ and is populated. Re-copy with cp .env samples/v1/javascript/.env.
Access denied due to invalid subscription key	Verify CONTENTUNDERSTANDING_ENDPOINT URL is correct. Check API key or run az login.
Model deployment not found	Deploy required models in Microsoft Foundry. Run updateDefaults.js.
Cognitive Services User role not assigned	Add the role in Azure Portal → Your resource → Access Control (IAM).
pnpm: command not found (only on --local install path)	Install with npm install -g pnpm.

Related Skills

• cu-sdk-sample-run - Run individual samples (including updateDefaults for model deployment setup)
• cu-sdk-common-knowledge - Domain knowledge for Content Understanding concepts

Additional Resources

• SDK README - Full documentation
• Samples directory - JavaScript sample files
• Product Documentation
• Prebuilt Analyzers