cu-sdk-sample-run

name	cu-sdk-sample-run
description	Run a specific sample for the Azure AI Content Understanding Java SDK. Use when users want to run a particular sample like Sample02_AnalyzeUrl or Sample03_AnalyzeInvoice.

Run a Specific Sample

Run a specific sample from the Azure AI Content Understanding Java SDK.

[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

• Java >= 8 (JDK)
• Maven
• SDK package available (public Maven Central or local build)
• Environment variables configured (via shell export)
• For prebuilt analyzers: model deployments configured (run Sample00_UpdateDefaults first)

[ASK USER] Prerequisites check: Before proceeding, verify the user's environment:

1. "Do you have Java and Maven installed?" -- If no, direct them to install JDK 8+ and Maven.
2. "Have you built the SDK or is it available on Maven Central?" -- If no, direct them to Step 2 below.
3. "Have you configured your environment variables (endpoint and credentials)?" -- If no, direct them to Step 3.
4. "Have you run Sample00_UpdateDefaults to configure model defaults?" -- If no and they want to use prebuilt analyzers, guide them to run it first.
5. (Deferred — only if the user later picks Sample16_CreateAnalyzerWithLabels.) "Do you plan to train with labeled data? If yes, you'll need an Azure Blob container with the receipt label files uploaded and a SAS URL." Walk them through Step 5's Sample16 subsection when relevant.

Package Directory

sdk/contentunderstanding/azure-ai-contentunderstanding

Available Samples

All sync samples have async versions with an Async suffix. Samples are located in:

src/samples/java/com/azure/ai/contentunderstanding/samples/

Getting Started (Run These First)

Sample00_UpdateDefaults -- Required First!

One-time setup - Configures model deployment mappings (GPT-4.1, GPT-4.1-mini, text-embedding-3-large) for your Microsoft Foundry resource. Must run before using prebuilt analyzers.

Sample02_AnalyzeUrl -- Start Here!

Analyzes content from a URL using prebuilt-documentSearch. Works with documents, images, audio, and video.

• Key concepts: URL input, markdown extraction, multi-modal content

Sample01_AnalyzeBinary

Analyzes local PDF/image files using prebuilt-documentSearch.

• Key concepts: Binary input, local file reading, page properties

Document Analysis

Sample03_AnalyzeInvoice

Extracts structured fields from invoices using prebuilt-invoice.

• Key concepts: Field extraction (customer name, totals, dates, line items), confidence scores, array fields

Sample10_AnalyzeConfigs

Extracts advanced features: charts, hyperlinks, formulas, annotations.

• Key concepts: Chart.js output, LaTeX formulas, PDF annotations, enhanced analysis options

Sample11_AnalyzeReturnRawJson

Gets raw JSON response for custom processing.

• Key concepts: Raw response access, saving to file, debugging

Custom Analyzers

Sample04_CreateAnalyzer

Creates custom analyzer with field schema for domain-specific extraction.

• Key concepts: Field types (string, number, date, object, array), extraction methods (extract, generate, classify)

Sample05_CreateClassifier

Creates classifier to categorize documents (Loan_Application, Invoice, Bank_Statement).

• Key concepts: Content categories, segmentation, document routing

Sample16_CreateAnalyzerWithLabels

Builds an analyzer using labeled training data loaded from Azure Blob Storage. The repo ships labeled receipt data at src/samples/resources/receipt_labels/ (*.jpg, *.jpg.labels.json, optional *.jpg.result.json).

• Key concepts: LabeledDataKnowledgeSource, knowledge sources on ContentAnalyzerConfig, container SAS URLs, optional path prefix, falls back to creating analyzer without training data if SAS URL is unset
• Requires either: (a) a SAS URL for an Azure Blob container with labeled data uploaded, or (b) accepting that no training data is used
• For an easier labeling workflow, use Azure AI Content Understanding Studio

Analyzer Management

Sample06_GetAnalyzer

Retrieves analyzer details and configuration.

Sample07_ListAnalyzers

Lists all analyzers in the Content Understanding resource.

• Key concepts: Paginated listing, analyzer enumeration

Sample08_UpdateAnalyzer

Updates analyzer description and tags.

Sample09_DeleteAnalyzer

Deletes a custom analyzer.

Sample14_CopyAnalyzer

Copies analyzer within the same resource.

Sample15_GrantCopyAuth

Cross-resource copying between different Azure resources/regions.

• Requires additional env vars: CONTENTUNDERSTANDING_SOURCE_RESOURCE_ID, CONTENTUNDERSTANDING_SOURCE_REGION, CONTENTUNDERSTANDING_TARGET_ENDPOINT, CONTENTUNDERSTANDING_TARGET_RESOURCE_ID, CONTENTUNDERSTANDING_TARGET_REGION, CONTENTUNDERSTANDING_TARGET_KEY (optional)

Result Management

Sample12_GetResultFile

Retrieves keyframe images from video analysis.

• Key concepts: Operation IDs, extracting generated files

Sample13_DeleteResult

Deletes analysis results for data cleanup.

• Key concepts: Result retention (24-hour auto-deletion), compliance

Advanced Helpers

Sample_Advanced_ToLlmInput

Advanced usage of the LlmInputHelper.toLlmInput helper that converts an AnalysisResult into LLM-ready text. For introductory usage, see Sample01_AnalyzeBinary, Sample03_AnalyzeInvoice, and Sample05_CreateClassifier.

• Key concepts: ToLlmInputOptions, content ranges, multi-modal flattening, prompt-friendly formatting

Workflow

Step 1: Navigate to Package Directory

cd sdk/contentunderstanding/azure-ai-contentunderstanding

Step 2: Build the SDK Package

The SDK package must be available for Maven to resolve. It will be published to Maven Central — if it's already available there, Maven will download it automatically and you can skip this step.

If the package is not yet published (or you want to test local changes), build and install it to your local Maven repository. The recommended command (run from the azure-sdk-for-java repo root) is:

cd ~/repos/azure-sdk-for-java   # or wherever you cloned the repo
mvn install -DskipTests -pl sdk/contentunderstanding/azure-ai-contentunderstanding -am

Tip: Building from the repo root with -pl ... -am is preferred when you are contributing across modules or testing in-repo dependency changes (e.g., a local azure-core patch). For most users, mvn install -DskipTests from within sdk/contentunderstanding/azure-ai-contentunderstanding also works, since this module's parent POM is resolved via relativePath and its runtime dependencies (e.g., azure-core) come from published artifacts.

[ASK USER] Build check: Ask: "Is the package already published on Maven Central, or do you need to build locally?"

• If published: Skip to Step 3.
• If not published / unsure: Run mvn install -DskipTests above and confirm it shows BUILD SUCCESS.

If the build fails, common fixes:

• Missing JDK: ensure java -version shows JDK 8+
• Missing Maven: ensure mvn -version works
• Parent POM not found: run mvn install -DskipTests -f ../../parents/azure-client-sdk-parent/pom.xml first

Step 3: Configure Environment Variables

[ASK USER] Configuration check: Ask the user: "Do you already have your environment variables configured (.env file or exported in shell)?"

• If yes: Skip to Step 4.
• If no: Direct them to the cu-sdk-setup skill for interactive setup, or guide them through the steps below.

Java samples read credentials from OS environment variables via System.getenv(). Java does not load .env files automatically, so the variables must be present in the shell environment when the JVM starts.

The recommended approach is to create a .env file and source it before running samples.

Tip: Use the cu-sdk-setup skill for an interactive walkthrough that creates your .env file step by step.

Create a .env file in the package root (sdk/contentunderstanding/azure-ai-contentunderstanding/.env):

# Azure AI Content Understanding - Environment Variables

# Required: Your Microsoft Foundry resource endpoint
CONTENTUNDERSTANDING_ENDPOINT=https://your-foundry.services.ai.azure.com/

# Optional: API key (leave empty to use DefaultAzureCredential via az login)
CONTENTUNDERSTANDING_KEY=

# Model deployment names (used by Sample00_UpdateDefaults)
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

Then load it into your shell:

set -a && source .env && set +a

Note: You must re-run set -a && source .env && set +a each time you open a new terminal or edit .env.

Alternative: export variables directly (without .env file)

Linux / macOS:

export CONTENTUNDERSTANDING_ENDPOINT="https://your-foundry.services.ai.azure.com/"
export CONTENTUNDERSTANDING_KEY=""   # Leave empty to use DefaultAzureCredential

export GPT_4_1_DEPLOYMENT="gpt-4.1"
export GPT_4_1_MINI_DEPLOYMENT="gpt-4.1-mini"
export TEXT_EMBEDDING_3_LARGE_DEPLOYMENT="text-embedding-3-large"

Windows (PowerShell):

$env:CONTENTUNDERSTANDING_ENDPOINT = "https://your-foundry.services.ai.azure.com/"
$env:CONTENTUNDERSTANDING_KEY = ""   # Leave empty to use DefaultAzureCredential

$env:GPT_4_1_DEPLOYMENT = "gpt-4.1"
$env:GPT_4_1_MINI_DEPLOYMENT = "gpt-4.1-mini"
$env:TEXT_EMBEDDING_3_LARGE_DEPLOYMENT = "text-embedding-3-large"

[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/
• If the user does not know where to find it: direct them to Azure Portal → Their Foundry resource → Keys and Endpoint.

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

• Option A: DefaultAzureCredential (recommended) — Uses az login or managed identity. No API key needed. Make sure you have run az login.
• Option B: API Key — Provide your CONTENTUNDERSTANDING_KEY from the Azure Portal → Keys and Endpoint → Key1 or Key2. Update .env so CONTENTUNDERSTANDING_KEY=<your-key> (replace the empty default).

[ASK USER] Confirm env vars: After the user sets their variables, ask: "Does this configuration look correct?" Wait for confirmation before proceeding.

Step 4: Choose the Sample

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

• Sample00_UpdateDefaults — Configure model defaults (one-time setup, required first)
• Sample02_AnalyzeUrl — Analyze content from a URL (recommended for first-time users)
• Sample01_AnalyzeBinary — Analyze a local PDF/image file
• Sample03_AnalyzeInvoice — Extract structured fields from an invoice
• Sample04_CreateAnalyzer — Create a custom analyzer
• Sample16_CreateAnalyzerWithLabels — Create an analyzer with labeled training data
• Other — Let me see the full list

[ASK USER] Sync or async?: Ask: "Would you like to run the sync or async version of this sample?"

• Sync (default) — e.g., Sample02_AnalyzeUrl
• Async — e.g., Sample02_AnalyzeUrlAsync

Step 5: Configure Sample-Specific Settings

Most samples only need the base environment variables from Step 3. The following samples require additional configuration before running.

[ASK USER] Sample-specific config: Based on the sample chosen in Step 4, walk the user through the matching subsection below:

• Prebuilt-analyzer samples — Sample02_AnalyzeUrl, Sample01_AnalyzeBinary, Sample03_AnalyzeInvoice, Sample10_AnalyzeConfigs, Sample11_AnalyzeReturnRawJson, Sample12_GetResultFile, Sample13_DeleteResult → "Have you run Sample00_UpdateDefaults?" subsection
• Sample01_AnalyzeBinary, Sample10_AnalyzeConfigs → also "Samples that need a local file" subsection
• Sample15_GrantCopyAuth → "Sample15_GrantCopyAuth cross-resource environment" subsection
• Sample16_CreateAnalyzerWithLabels → "Sample16_CreateAnalyzerWithLabels training data" subsection
• Sample00_UpdateDefaults — sets up the model defaults itself; only the base env vars from Step 3 are needed
• Custom-analyzer samples (Sample04_CreateAnalyzer, Sample05_CreateClassifier) and management samples (Sample06–Sample09, Sample14) — only the base env vars from Step 3 are needed

If none apply, proceed directly to Step 6.

Settings by sample

Setting	Required By	Description
CONTENTUNDERSTANDING_ENDPOINT	All samples	Your Microsoft Foundry resource endpoint URL
CONTENTUNDERSTANDING_KEY	All samples (optional)	API key for key-based auth. If empty, DefaultAzureCredential is used (recommended — run az login first)
GPT_4_1_DEPLOYMENT	Sample00_UpdateDefaults	Deployment name for gpt-4.1 model (default: gpt-4.1)
GPT_4_1_MINI_DEPLOYMENT	Sample00_UpdateDefaults	Deployment name for gpt-4.1-mini model (default: gpt-4.1-mini)
TEXT_EMBEDDING_3_LARGE_DEPLOYMENT	Sample00_UpdateDefaults	Deployment name for text-embedding-3-large model (default: text-embedding-3-large)
CONTENTUNDERSTANDING_SOURCE_RESOURCE_ID	Sample15_GrantCopyAuth	Source ARM resource ID for cross-resource copy
CONTENTUNDERSTANDING_SOURCE_REGION	Sample15_GrantCopyAuth	Region of the source Foundry resource (e.g., westus)
CONTENTUNDERSTANDING_TARGET_ENDPOINT	Sample15_GrantCopyAuth	Target Foundry resource endpoint for cross-resource copy
CONTENTUNDERSTANDING_TARGET_RESOURCE_ID	Sample15_GrantCopyAuth	Target ARM resource ID for cross-resource copy
CONTENTUNDERSTANDING_TARGET_REGION	Sample15_GrantCopyAuth	Region of the target Foundry resource (e.g., eastus)
CONTENTUNDERSTANDING_TARGET_KEY	Sample15_GrantCopyAuth (optional)	API key for the target resource. If empty, DefaultAzureCredential is used
CONTENTUNDERSTANDING_TRAINING_DATA_SAS_URL	Sample16 (Option A)	Pre-generated container-level SAS URL pointing at your labeled training data. If set, the sample uses it directly and skips Option B.

Have you run Sample00_UpdateDefaults?

Most samples that use prebuilt analyzers (e.g., Sample02_AnalyzeUrl, Sample03_AnalyzeInvoice, Sample10_AnalyzeConfigs, Sample11_AnalyzeReturnRawJson) require model deployments to be configured. Sample00_UpdateDefaults writes a one-time mapping from logical model names (gpt-4.1, gpt-4.1-mini, text-embedding-3-large) to your Foundry resource's actual deployment names. Without it, prebuilt analyzers fail with Model deployment not found.

[ASK USER] Update defaults check: Ask: "Have you previously run Sample00_UpdateDefaults for this Foundry resource?"

• If yes: Continue to the next subsection (or Step 6 if none apply).
• If no and the chosen sample uses prebuilt analyzers:
  1. Run Sample00_UpdateDefaults now using the command in Step 6: mvn exec:java -Dexec.mainClass="com.azure.ai.contentunderstanding.samples.Sample00_UpdateDefaults" -Dexec.classpathScope=test
  2. Wait for it to print success.
  3. Then come back to Step 4, re-select the original sample the user wanted, and continue from Step 5.

Samples that need a local file

The Sample01_AnalyzeBinary and Sample10_AnalyzeConfigs samples load a local file from src/samples/resources/. The default file paths are built into the samples. To use your own file, update the filePath variable in the sample code.

[ASK USER] Local file (if applicable): If the user chose a sample that requires a local file (Sample01_AnalyzeBinary, Sample10_AnalyzeConfigs), ask: "This sample requires a local document file. Would you like to:"

• Use the default test file — The sample has a built-in file path under src/samples/resources/.
• Provide your own file — You'll need to update the filePath variable in the sample code.

Setting up Sample15_GrantCopyAuth cross-resource environment

The Sample15_GrantCopyAuth sample requires two separate Microsoft Foundry resources (source and target).

Add the following environment variables to your .env file:

CONTENTUNDERSTANDING_SOURCE_RESOURCE_ID=/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.CognitiveServices/accounts/{sourceAccountName}
CONTENTUNDERSTANDING_SOURCE_REGION=westus
CONTENTUNDERSTANDING_TARGET_ENDPOINT=https://your-target-foundry.services.ai.azure.com/
CONTENTUNDERSTANDING_TARGET_RESOURCE_ID=/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.CognitiveServices/accounts/{targetAccountName}
CONTENTUNDERSTANDING_TARGET_REGION=eastus
# Optional — only if you want key-based auth for the target resource:
# CONTENTUNDERSTANDING_TARGET_KEY=<your-target-resource-key>

Then reload your shell: set -a && source .env && set +a.

[ASK USER] Cross-resource setup (Sample15_GrantCopyAuth only): If the user chose Sample15_GrantCopyAuth, ask:

1. "Do you have two separate Microsoft Foundry resources (source and target) set up?" — If no, guide them to create a second resource.
2. "Please provide the source ARM Resource ID and region, and the target endpoint URL, ARM Resource ID, and region."
3. "Will you authenticate the target resource with DefaultAzureCredential (recommended) or with CONTENTUNDERSTANDING_TARGET_KEY?"
4. Confirm: "Both resources must have the Cognitive Services User role assigned if using DefaultAzureCredential. Is this configured?"

Setting up Sample16_CreateAnalyzerWithLabels training data

The Sample16_CreateAnalyzerWithLabels sample creates an analyzer backed by labeled training data loaded from Azure Blob Storage via a SAS URL. You can configure training data in two ways:

• Option A — Manual upload: you upload the labeled triplets (image + .labels.json + .result.json) yourself and provide a container SAS URL via CONTENTUNDERSTANDING_TRAINING_DATA_SAS_URL.
• Option B — Auto-upload via DefaultAzureCredential: the sample uses your az login identity to upload the bundled receipt files from src/samples/resources/receipt_labels/ into your storage account and mint a short-lived User Delegation SAS — set CONTENTUNDERSTANDING_TRAINING_DATA_STORAGE_ACCOUNT and CONTENTUNDERSTANDING_TRAINING_DATA_CONTAINER. The signed-in identity must have Storage Blob Data Contributor on the container.

Note: If neither option is configured, the sample runs in demo mode: it still creates the analyzer (without labeled data) so you can see the API surface. To fully exercise the labeled-data path you must pick Option A or Option B.

The repo ships labeled receipt training data at src/samples/resources/receipt_labels/. Two labeled receipts are included; each receipt has three associated files:

17a84146-e910-460c-bf80-a625e6f64fea.jpg          # original image
17a84146-e910-460c-bf80-a625e6f64fea.jpg.labels.json  # labeled fields (required)
17a84146-e910-460c-bf80-a625e6f64fea.jpg.result.json  # OCR result (optional)
29d60394-3da1-4714-abdc-ff0993009872.jpg
29d60394-3da1-4714-abdc-ff0993009872.jpg.labels.json
29d60394-3da1-4714-abdc-ff0993009872.jpg.result.json

Option A — manual upload steps:

1. Create an Azure Blob Storage container (or use an existing one).
2. Upload all files from src/samples/resources/receipt_labels/ (the .jpg, .jpg.labels.json, and optional .jpg.result.json files listed above) into the container. You may upload them at the container root or inside a subfolder (e.g., receipt_labels/).
3. In Azure Portal: open the storage account, then either navigate Storage account → Containers → your container → Shared access tokens, or use the Portal search bar to find "Shared access tokens" (the exact UI path varies by Portal version). Set an expiry, grant at least List and Read permissions, then generate the SAS URL.
4. Add the SAS URL to your .env file:

   CONTENTUNDERSTANDING_TRAINING_DATA_SAS_URL=https://<account>.blob.core.windows.net/<container>?sv=...&se=...
   # Only if you uploaded into a subfolder:
   CONTENTUNDERSTANDING_TRAINING_DATA_PREFIX=receipt_labels
   

   (Both receipt_labels and receipt_labels/ work as prefix values — the SDK handles the trailing slash either way.)
5. Reload your shell: set -a && source .env && set +a.

Option B — auto-upload via DefaultAzureCredential:

1. Ensure az login has been completed and your account has Storage Blob Data Contributor on the target container (or the parent storage account).
2. Add to your .env:

   CONTENTUNDERSTANDING_TRAINING_DATA_STORAGE_ACCOUNT=mystorageacct
   CONTENTUNDERSTANDING_TRAINING_DATA_CONTAINER=cu-training-data
   # Leave CONTENTUNDERSTANDING_TRAINING_DATA_SAS_URL empty/unset to trigger Option B.
   # Optional: override the local folder uploaded (defaults to src/samples/resources/receipt_labels).
   # CONTENTUNDERSTANDING_TRAINING_DATA_LOCAL_DIR=/path/to/your/labels
   # Optional: upload into a sub-folder (e.g., receipt_labels) instead of the container root.
   # CONTENTUNDERSTANDING_TRAINING_DATA_PREFIX=receipt_labels
   

3. Reload your shell: set -a && source .env && set +a.
4. The sample will create the container if it does not exist, upload the bundled files, mint a 1-hour User Delegation SAS, and use it to create the analyzer.

[REQUIRED GATE] Sample16 training data (Sample16_CreateAnalyzerWithLabels only): Sample16 silently falls back to "create analyzer without labeled data" when no training-data source is configured. That fall-back path completes end-to-end and prints ✓ Sample completed even though the labeled-data API surface is not actually exercised. Before invoking mvn exec:java (or run_sample.sh Sample16_CreateAnalyzerWithLabels --run), the agent must ask the user the questions below and act on the answer:

1. "Do you want to train with labeled data (recommended), or create the analyzer without training data (demo mode)?"
   • If demo mode: confirm explicitly — "I will run Sample16 without training data. The output will say Knowledge sources: 0 and you will see a DEMO MODE banner. The labeled-data API path will not be exercised. OK to proceed?" Only continue after the user says yes; leave both Option A and Option B env vars empty/unset.
   • If with training data: continue with one of the next two questions.
2. "Will you use Option A (pre-generated SAS URL) or Option B (auto-upload via DefaultAzureCredential)?"
   • Option A: ask for the SAS URL and (optionally) prefix; walk through the manual-upload steps above if not yet done.
   • Option B: ask for the storage account name and container name; remind them about the Storage Blob Data Contributor role and az login.
3. "Did you upload the files at the container root or inside a subfolder?"
   • If root: leave CONTENTUNDERSTANDING_TRAINING_DATA_PREFIX unset.
   • If subfolder: ask for the prefix path (e.g., receipt_labels/).
4. Confirm: "For Option A, the SAS token must have at least List and Read permissions and must not be expired. For Option B, the signed-in identity must have Storage Blob Data Contributor on the container."

Belt-and-suspenders: run_sample.sh itself emits a loud DEMO MODE banner before the mvn exec:java step when none of the four training-data env vars (, , , ) are set, so the fall-back is unmissable in the captured run output. Treat that banner as a signal that validation is unless the user explicitly opted into demo mode in step 1.

Step 6: Run the Sample

Run the sample with Maven directly:

# Make sure .env is loaded first (if not already done in Step 3)
set -a && source .env && set +a

# From the package directory: sdk/contentunderstanding/azure-ai-contentunderstanding
mvn exec:java -Dexec.mainClass="com.azure.ai.contentunderstanding.samples.Sample02_AnalyzeUrl" -Dexec.classpathScope=test

More examples:

# Run async sample
mvn exec:java -Dexec.mainClass="com.azure.ai.contentunderstanding.samples.Sample02_AnalyzeUrlAsync" -Dexec.classpathScope=test

# Run update defaults (one-time setup)
mvn exec:java -Dexec.mainClass="com.azure.ai.contentunderstanding.samples.Sample00_UpdateDefaults" -Dexec.classpathScope=test

# Run invoice extraction
mvn exec:java -Dexec.mainClass="com.azure.ai.contentunderstanding.samples.Sample03_AnalyzeInvoice" -Dexec.classpathScope=test

# Run analyzer with labeled training data
mvn exec:java -Dexec.mainClass="com.azure.ai.contentunderstanding.samples.Sample16_CreateAnalyzerWithLabels" -Dexec.classpathScope=test

Note: The -Dexec.classpathScope=test flag is required. Samples live in src/samples/, which is compiled as a test source root — not part of the main classpath. This is an Azure SDK for Java convention: samples are not shipped in the published JAR, and they depend on test-scoped dependencies (e.g., azure-identity). Without this flag, Maven cannot find the sample classes and will fail with ClassNotFoundException.

Note: Maven inherits the current shell's environment variables. System.getenv() in the sample code reads these values at runtime, so your .env must be sourced in the same terminal session before running mvn.

Alternative: use the helper script (optional)

The run_sample.sh script is a convenience wrapper around mvn exec:java. It resolves the class name, validates the sample exists, and optionally loads .env files.

# Run a sample
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh Sample02_AnalyzeUrl

# Run with .env file (auto-loads environment variables into the shell)
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh Sample02_AnalyzeUrl --env .env

# List all available samples
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh --list

After the Sample Runs — Review Results and Explain the Sample

After the sample completes, the skill must do the following for the user (do not skip):

1. Show the terminal command to re-run this sample directly, so the user can iterate without the skill. For example:

   set -a && source .env && set +a
   mvn exec:java -Dexec.mainClass="com.azure.ai.contentunderstanding.samples.Sample02_AnalyzeUrl" -Dexec.classpathScope=test
   

   Substitute Sample02_AnalyzeUrl with the sample the user just ran.

2. Briefly explain the key code concepts demonstrated in the sample. Tailor the explanation to the specific sample; common concepts include:

   • Client creation — how ContentUnderstandingClient is constructed via the builder (endpoint + DefaultAzureCredentialBuilder or AzureKeyCredential)
   • Analyzer selection — which prebuilt (prebuilt-documentSearch, prebuilt-invoice, etc.) or custom analyzer is used and why
   • Input type — URL vs. BinaryData vs. local file
   • Result processing — how the returned AnalyzeResult is traversed (pages, fields, contents)
   • Content type casting — e.g., casting AnalyzedContent to AnalyzedDocumentContent / AnalyzedImageContent / AnalyzedAudioContent / AnalyzedVideoContent when needed
   • Long-running operation polling — if the sample uses SyncPoller / beginAnalyze

[ASK USER] Sample result: Ask: "Did the sample run successfully?"

• If yes: present the re-run command and the key-code explanation (above), then ask: "Would you like to run another sample, or are you all set?"
• If no: help troubleshoot using the Troubleshooting section below. Common issues include missing environment variables, SDK not built, or model defaults not configured.

[ASK USER] Run another?: If the user wants to run another sample, loop back to the "Which sample?" prompt above.

Quick Reference

Most Common Samples for New Users

1. First-time setup (run once per Foundry resource):

   mvn exec:java -Dexec.mainClass="com.azure.ai.contentunderstanding.samples.Sample00_UpdateDefaults" -Dexec.classpathScope=test
   

2. Analyze a document from URL:

   mvn exec:java -Dexec.mainClass="com.azure.ai.contentunderstanding.samples.Sample02_AnalyzeUrl" -Dexec.classpathScope=test
   

3. Analyze a local PDF file:

   mvn exec:java -Dexec.mainClass="com.azure.ai.contentunderstanding.samples.Sample01_AnalyzeBinary" -Dexec.classpathScope=test
   

4. Extract invoice fields:

   mvn exec:java -Dexec.mainClass="com.azure.ai.contentunderstanding.samples.Sample03_AnalyzeInvoice" -Dexec.classpathScope=test
   

Scripts (Optional)

Helper scripts are provided in scripts/ as a convenience. They are not required — you can always use mvn exec:java directly.

Note: For first-time environment setup (installing JDK/Maven, building the SDK, creating .env), use the cu-sdk-setup skill.

run_sample.sh -- Run a Sample with Conveniences

Wraps mvn exec:java with sample name resolution, validation, and optional .env loading.

# Run a sample (resolves class name automatically)
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh Sample02_AnalyzeUrl

# Load env vars from .env file before running
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh Sample02_AnalyzeUrl --env .env

# List available samples
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh --list

# Dry run (show what would be executed)
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh Sample02_AnalyzeUrl --dry-run

Troubleshooting

Error	Solution
BUILD FAILURE during compile	Ensure JDK 8+ and Maven are installed; run mvn install -DskipTests from the package directory
ClassNotFoundException or NoClassDefFoundError	Add -Dexec.classpathScope=test to the mvn exec:java command. Samples are compiled as test sources (Azure SDK convention) and are not on the main classpath. If still failing, rebuild with: mvn compile test-compile
CONTENTUNDERSTANDING_ENDPOINT is null	Set the environment variable: export CONTENTUNDERSTANDING_ENDPOINT="https://..."
Access denied or authorization errors	Ensure Cognitive Services User role is assigned; check API key or run az login
Model deployment not found	Run Sample00_UpdateDefaults first to configure model mappings
FileNotFoundException for binary samples	Run samples from the package root directory (sdk/contentunderstanding/azure-ai-contentunderstanding)
Parent POM not resolved	Run mvn install -DskipTests -f ../../parents/azure-client-sdk-parent/pom.xml first
Permission denied when running scripts	Make scripts executable: chmod +x .github/skills/cu-sdk-sample-run/scripts/*.sh
Sample16: AuthenticationFailed / 403 reading training data	The SAS URL is invalid, expired, or missing required permissions. Regenerate the SAS with at least List and Read and a fresh expiry, then re-source .env
Sample16: BlobNotFound or empty training set	The CONTENTUNDERSTANDING_TRAINING_DATA_PREFIX does not match where you uploaded the files. Either upload files at the container root and unset the prefix, or set the prefix to the actual subfolder (e.g., receipt_labels/)
Sample16: created analyzer has no training data

Related Skills

• cu-sdk-setup — Interactive .env file setup (configure endpoint, auth, and model deployments before running samples)
• cu-sdk-common-knowledge — Domain knowledge for Content Understanding concepts

Additional Resources

• SDK README — Full SDK documentation
• Product Documentation
• Azure SDK for Java Contributing Guide