| name | cu-sdk-sample-run |
| description | Run a specific sample for the Azure AI Content Understanding SDK. Use when users want to run a particular sample like sample_analyze_url.py or sample_analyze_invoice.py. |
Run a Specific Sample
Run a specific sample from the Azure AI Content Understanding 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
- Python >= 3.9
- Virtual environment set up with SDK installed (see
cu-sdk-setup skill)
- Environment variables configured in
.env
- Model deployments configured via
sample_update_defaults.py -- required for any sample that uses GPT-4.1 / text-embedding-3-large (prebuilt analyzers like prebuilt-invoice, custom analyzers with extract / generate / classify field methods, and sample_create_analyzer_with_labels)
[ASK USER] Prerequisites check (run BEFORE Step 1 of the Workflow below):
Before starting the numbered Workflow, verify the user's environment by asking these three questions in order. Do NOT skip Step 1 until each has a yes/handled answer:
- "Have you already set up your Python environment and installed the SDK?" -- If no, direct them to the
cu-sdk-setup skill first; pause this skill until that finishes.
- "Have you configured your
.env file with your endpoint and credentials?" -- If no, direct them to Step 4 of the cu-sdk-setup skill.
- "Have you run
sample_update_defaults.py to configure model defaults?" -- Defer judgement: ask once you know which sample they want (Step 3 below), then check it against the "Samples that require sample_update_defaults first" list in Step 4.
Package Directory
sdk/contentunderstanding/azure-ai-contentunderstanding
Available Samples
All sync samples have async versions with _async suffix in samples/async_samples/.
Getting Started (Run These First)
sample_update_defaults -- 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.
sample_analyze_url -- 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
sample_analyze_binary
Analyzes local PDF/image files using prebuilt-documentSearch.
- Key concepts: Binary input, local file reading, page properties
Document Analysis
sample_analyze_invoice
Extracts structured fields from invoices using prebuilt-invoice.
- Key concepts: Field extraction (customer name, totals, dates, line items), confidence scores, array fields
sample_analyze_configs
Extracts advanced features: charts, hyperlinks, formulas, annotations.
- Key concepts: Chart.js output, LaTeX formulas, PDF annotations, enhanced analysis options
sample_analyze_return_raw_json
Gets raw JSON response for custom processing.
- Key concepts: Raw response access, saving to file, debugging
sample_to_llm_input
Advanced usage of the to_llm_input helper -- converts structured AnalysisResult into LLM-ready text (YAML front matter + markdown body).
- Key concepts: LLM integration, output options (fields-only, markdown-only, custom metadata), multi-segment video/audio rendering, page markers via
content_range
- Python-only helper (not available in other language SDKs)
Custom Analyzers
sample_create_analyzer
Creates custom analyzer with field schema for domain-specific extraction.
- Key concepts: Field types (string, number, date, object, array), extraction methods (extract, generate, classify)
- Next step: to run this workflow on your own folder of documents (validate schema → create → batch test → stdout summary), use the
cu-sdk-author-analyzer skill.
sample_create_classifier
Creates classifier to categorize documents (Loan_Application, Invoice, Bank_Statement).
- Key concepts: Content categories, segmentation, document routing
- Next step: to run this workflow on your own mixed-document packets, use the
cu-sdk-author-analyzer-classify-route skill.
sample_create_analyzer_with_labels
Builds analyzers with training labels (labeled data from Azure Blob Storage).
- Key concepts: Labeled data, knowledge sources, Blob Storage SAS URIs
- Requires additional env vars:
CONTENTUNDERSTANDING_TRAINING_DATA_SAS_URL (Option A) or CONTENTUNDERSTANDING_TRAINING_DATA_STORAGE_ACCOUNT + CONTENTUNDERSTANDING_TRAINING_DATA_CONTAINER (Option B)
Analyzer Management
sample_get_analyzer
Retrieves analyzer details and configuration.
sample_list_analyzers
Lists all available analyzers (prebuilt and custom).
sample_update_analyzer
Updates analyzer description and tags.
sample_delete_analyzer
Deletes a custom analyzer.
sample_copy_analyzer
Copies analyzer within the same resource.
sample_grant_copy_auth
Cross-resource copying between different Azure resources/regions.
- Requires additional env vars:
CONTENTUNDERSTANDING_TARGET_ENDPOINT, CONTENTUNDERSTANDING_TARGET_RESOURCE_ID
Result Management
sample_get_result_file
Retrieves keyframe images from video analysis.
- Key concepts: Operation IDs, extracting generated files
sample_delete_result
Deletes analysis results for data cleanup.
- Key concepts: Result retention (24-hour auto-deletion), compliance
Workflow
Step 1: Navigate to Package Directory
cd sdk/contentunderstanding/azure-ai-contentunderstanding
Step 2: Activate Virtual Environment
source .venv/bin/activate
[ASK USER] Confirm venv active:
Ask: "Is your virtual environment active? Run which python (or where python on Windows) and confirm it points to a path inside .venv."
If the user reports it is not active or does not exist, direct them to the cu-sdk-setup skill.
Step 3: Choose the Sample
Pick the sample (and sync/async variant) before configuring sample-specific settings -- subsequent [ASK USER] blocks in Step 4 are gated on the chosen sample.
[ASK USER] Which sample?:
If the user has already named a specific sample (e.g., "run sample_analyze_invoice"), confirm rather than re-prompt:
"You want to run <sample_name>. Continuing with that -- correct? (Reply "no" to see the full list.)"
If the user has not yet specified a sample, ask: "Which sample would you like to run?" with options:
sample_analyze_url -- Analyze content from a URL (recommended for first-time users)sample_analyze_binary -- Analyze a local PDF/image filesample_analyze_invoice -- Extract structured fields from an invoicesample_create_analyzer -- Create a custom analyzersample_create_analyzer_with_labels -- Create a labeled-data analyzer (auto-uploads training data)sample_update_defaults -- Configure model defaults (one-time setup)- Other -- Let me see the full list
If the user picks "Other" (or replies "no" to the confirmation), show the full Available Samples list above or run the --list command (path note: from package root run .github/skills/cu-sdk-sample-run/scripts/run_sample.sh --list; from the workspace root prepend sdk/contentunderstanding/azure-ai-contentunderstanding/).
[ASK USER] Sync or async?:
Ask: "Would you like to run the sync or async version of this sample?"
- Sync (default) -- Runs in
samples/
- Async -- Runs in
samples/async_samples/ with _async suffix
Step 4: Configure Sample-Specific Settings (if needed)
Now that the chosen sample is known, run only the subsections below that apply to it. Each subsection's [ASK USER] block is already gated on the relevant sample(s); skip a subsection if it doesn't match. For full environment setup (creating .venv, installing the SDK, writing .env), use the cu-sdk-setup skill. The subsections below cover settings that are sample-specific and may not have been configured during the initial setup.
Samples that require sample_update_defaults first
These samples invoke an analyzer that depends on GPT-4.1 / GPT-4.1-mini / text-embedding-3-large model deployments. They will fail with a model deployment not found error unless sample_update_defaults.py has been run for the resource:
| Sample | Why |
|---|
sample_analyze_invoice | prebuilt-invoice uses GPT-4.1 for field extraction |
sample_analyze_configs | Advanced features (charts, formulas, etc.) use GPT-4.1 |
sample_create_analyzer | Custom field schema with extract / generate / classify methods |
sample_create_classifier | Document classification uses GPT-4.1 |
sample_create_analyzer_with_labels | Labeled-data analyzer uses GPT-4.1 + text-embedding-3-large |
Samples that do not require sample_update_defaults (safe to run first): sample_analyze_url, sample_analyze_binary, sample_analyze_return_raw_json, sample_to_llm_input, all analyzer-management samples (sample_get_*, sample_list_*, sample_update_analyzer, sample_delete_*, sample_copy_analyzer, sample_grant_copy_auth).
[ASK USER] Model defaults check (only if chosen sample is in the table above):
Ask: "This sample requires model deployments to be configured. Have you already run sample_update_defaults.py for this Microsoft Foundry resource?"
- Yes -- proceed.
- No -- run it first:
cd samples && python sample_update_defaults.py (only needed once per resource).
- Not sure -- it's safe to re-run; the call is idempotent.
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 | sample_update_defaults | Deployment name for gpt-4.1 model (default: gpt-4.1) |
GPT_4_1_MINI_DEPLOYMENT | sample_update_defaults | Deployment name for gpt-4.1-mini model (default: gpt-4.1-mini) |
TEXT_EMBEDDING_3_LARGE_DEPLOYMENT | sample_update_defaults | Deployment name for text-embedding-3-large model (default: text-embedding-3-large) |
CONTENTUNDERSTANDING_SOURCE_RESOURCE_ID | sample_grant_copy_auth | Source ARM resource ID for cross-resource copy |
CONTENTUNDERSTANDING_SOURCE_REGION | sample_grant_copy_auth | Source region (e.g., eastus) for cross-resource copy |
CONTENTUNDERSTANDING_TARGET_ENDPOINT | sample_grant_copy_auth | Target Foundry resource endpoint for cross-resource copy |
CONTENTUNDERSTANDING_TARGET_RESOURCE_ID | sample_grant_copy_auth | Target ARM resource ID for cross-resource copy |
CONTENTUNDERSTANDING_TARGET_REGION | sample_grant_copy_auth | Target region (e.g., swedencentral) for cross-resource copy |
CONTENTUNDERSTANDING_TARGET_KEY | sample_grant_copy_auth (optional) | Target API key. If empty, DefaultAzureCredential is used for the target resource |
CONTENTUNDERSTANDING_TRAINING_DATA_SAS_URL | sample_create_analyzer_with_labels (Option A) | SAS URL of the Blob container holding labeled training data (List + Read permissions) |
CONTENTUNDERSTANDING_TRAINING_DATA_STORAGE_ACCOUNT | sample_create_analyzer_with_labels (Option B) | Storage account name for auto-upload via DefaultAzureCredential |
CONTENTUNDERSTANDING_TRAINING_DATA_CONTAINER | sample_create_analyzer_with_labels (Option B) | Container name for auto-upload |
CONTENTUNDERSTANDING_TRAINING_DATA_PREFIX | sample_create_analyzer_with_labels (optional) | Virtual directory (prefix) within the container, e.g., training_samples/ |
Setting up sample_create_analyzer_with_labels training data
sample_create_analyzer_with_labels needs labeled training data in an Azure Blob container. Two configurations are supported -- the sample auto-detects which one to use based on which env vars are set.
[ASK USER] Training data option (sample_create_analyzer_with_labels only):
If the user chose sample_create_analyzer_with_labels, ask:
"How would you like to provide labeled training data?"
- Option A -- Pre-generated SAS URL (recommended for production): You already have labeled files in a Blob container and a SAS URL with Read + List permissions.
- Set
CONTENTUNDERSTANDING_TRAINING_DATA_SAS_URL in .env to the full URL (https://<account>.blob.core.windows.net/<container>?sv=...&se=...).
- Option B -- Auto-upload from local files (recommended for first-time users): The sample will upload the bundled
samples/sample_files/training_samples/ (receipts + label JSON) to your container and generate a User Delegation SAS automatically.
- Set
CONTENTUNDERSTANDING_TRAINING_DATA_STORAGE_ACCOUNT and CONTENTUNDERSTANDING_TRAINING_DATA_CONTAINER in .env.
- Optionally set
CONTENTUNDERSTANDING_TRAINING_DATA_PREFIX (e.g., training_samples/) to scope uploads to a virtual folder.
- Requires
DefaultAzureCredential to have Storage Blob Data Contributor on the storage account (run az login first).
If the user is unsure, recommend Option B. The next subsection ("Samples that need a local file") only applies if Option B is chosen.
Do not set both options. If both are configured, Option A (the SAS URL) takes precedence.
Samples that need a local file
The sample_analyze_binary and sample_analyze_configs samples load a local file from samples/sample_files/. The default file paths are built into the samples (sample_files/sample_invoice.pdf and sample_files/sample_document_features.pdf respectively). To use your own file, update the file_path variable in the sample code.
sample_create_analyzer_with_labels (Option B only) auto-uploads bundled labeled receipts from samples/sample_files/training_samples/ (receipt1.jpg, receipt1.jpg.labels.json, receipt2.jpg, receipt2.jpg.labels.json, etc.) to the configured Blob container. To use your own labeled data, replace the files under samples/sample_files/training_samples/ (keeping the <image>.labels.json naming convention) before running.
[ASK USER] Local file (if applicable):
If the user chose a sample that requires a local file (sample_analyze_binary, sample_analyze_configs, or sample_create_analyzer_with_labels and chose Option B in the previous subsection), ask:
"This sample uses local files from samples/sample_files/. Would you like to:"
- Use the default test files -- The sample has built-in paths under
samples/sample_files/.
- Provide your own files -- For
sample_analyze_binary / sample_analyze_configs, update the file_path variable in the sample code. For sample_create_analyzer_with_labels (Option B), replace the files under samples/sample_files/training_samples/.
Setting up sample_grant_copy_auth cross-resource environment
The sample_grant_copy_auth sample requires two separate Microsoft Foundry resources (source and target).
Add the following to your .env:
CONTENTUNDERSTANDING_SOURCE_RESOURCE_ID="/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.CognitiveServices/accounts/{sourceAccountName}"
CONTENTUNDERSTANDING_SOURCE_REGION="eastus"
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="swedencentral"
[ASK USER] Cross-resource setup (sample_grant_copy_auth only):
If the user chose sample_grant_copy_auth, ask:
- "Do you have two separate Microsoft Foundry resources (source and target) set up?" -- If no, guide them to create a second resource.
- "Your
CONTENTUNDERSTANDING_ENDPOINT will be used as the source endpoint. Please provide the following for your source resource:" -- Source ARM Resource ID, Source region.
- "Please provide the following for your target resource:" -- Target endpoint URL, Target ARM Resource ID, Target region.
- Confirm: "Cross-resource copy works with both
DefaultAzureCredential and API keys. Both resources must have the Cognitive Services User role assigned if using DefaultAzureCredential. Is this configured?"
[REQUIRED GATE] sample_create_analyzer_with_labels training data (sample_create_analyzer_with_labels only):
sample_create_analyzer_with_labels 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: sample_create_analyzer_with_labels even though the labeled-data API surface
is not actually exercised, AND the analyze-test step (begin_analyze against a sample invoice)
is also skipped. Before invoking python sample_create_analyzer_with_labels.py (or
run_sample.sh sample_create_analyzer_with_labels), the agent must ask the user the questions
below and act on the answer:
- "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
sample_create_analyzer_with_labels without training data. The output will say Knowledge sources: 0, the Testing analyzer with sample document... step will be skipped, 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.
- "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.
- "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.,
training_samples/).
- 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 storage account."
Belt-and-suspenders: run_sample.sh itself emits a loud DEMO MODE banner before the
python step when neither CONTENTUNDERSTANDING_TRAINING_DATA_SAS_URL (Option A) nor both
CONTENTUNDERSTANDING_TRAINING_DATA_STORAGE_ACCOUNT + CONTENTUNDERSTANDING_TRAINING_DATA_CONTAINER
(Option B) are set, so the fall-back is unmissable in the captured run output. Treat that
banner as a signal that validation is incomplete unless the user explicitly opted into
demo mode in step 1.
Step 5: Run the Sample
Run manually (recommended):
cd samples
python sample_analyze_url.py
cd samples/async_samples
python sample_analyze_url_async.py
Or use the script:
Path note: All run_sample.sh invocations below assume the package root (sdk/contentunderstanding/azure-ai-contentunderstanding) as the current directory. From the workspace root, prepend sdk/contentunderstanding/azure-ai-contentunderstanding/. The script itself resolves paths relative to its own location, so the working directory only affects how you type the command.
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh <sample_name>
Examples:
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh sample_analyze_url
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh sample_analyze_url_async
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh sample_analyze_invoice.py
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):
-
Show the terminal command to re-run this sample directly, so the user can iterate without the skill. For example:
cd samples && python sample_analyze_url.py
cd samples/async_samples && python sample_analyze_url_async.py
Substitute sample_analyze_url with the sample the user just ran.
-
Briefly explain the key code concepts demonstrated in the sample. Tailor the explanation to the specific sample; common concepts include:
- Client creation -- how the
ContentUnderstandingClient (or aio.ContentUnderstandingClient for async) is constructed (endpoint + DefaultAzureCredential or AzureKeyCredential)
- Analyzer selection -- which prebuilt (
prebuilt-documentSearch, prebuilt-invoice, etc.) or custom analyzer is used and why
- Input type -- URL vs. binary stream (
bytes) vs. local file
- Result processing -- how the returned
AnalyzeResult is traversed (pages, fields, contents)
- Content type narrowing -- e.g., narrowing
AnalyzedContent to AnalyzedDocumentContent / AnalyzedImageContent / AnalyzedAudioContent / AnalyzedVideoContent (using isinstance or content kind discrimination)
- Long-running operations -- if the sample uses
begin_analyze and the returned LROPoller (or AsyncLROPoller)
In addition to the generic concepts above, prefer the sample-specific highlights below when they apply:
| Sample | Sample-specific concepts to call out |
|---|
sample_analyze_url | URL-based analysis, multi-modal content (document / image / audio / video), markdown output |
sample_analyze_binary | Reading local files as bytes, page properties, content type narrowing |
sample_analyze_invoice | prebuilt-invoice field schema, Field confidence scores, array fields (line items) |
sample_analyze_configs | AnalysisConfig options (charts, formulas, hyperlinks, annotations), Chart.js / LaTeX outputs |
sample_analyze_return_raw_json | Accessing the raw JSON via cls=lambda pipeline_response, deserialized, _: pipeline_response.http_response.text() -- bypassing the typed model |
sample_to_llm_input | to_llm_input() helper, output options (fields-only / markdown-only / metadata), content_range page markers |
sample_update_defaults | update_defaults() -- mapping model aliases to your deployment names; idempotent one-time setup |
sample_create_analyzer | Analyzer schema, field types, extract / generate / classify field methods |
sample_create_classifier | ContentCategory definitions, document segmentation, classification routing |
sample_create_analyzer_with_labels | LabeledDataKnowledgeSource, User Delegation SAS auto-generation (Option B), labeled receipts upload pattern, combining labeled data with extract / generate field methods |
sample_get_analyzer / sample_list_analyzers | Listing prebuilt + custom analyzers, paging |
sample_update_analyzer | Patching analyzer description / tags |
sample_delete_analyzer / sample_delete_result | Cleanup patterns; result auto-deletion after 24h |
sample_copy_analyzer | Same-resource copy via begin_copy_analyzer |
sample_grant_copy_auth | Cross-resource copy: grant_copy_auth on source + begin_copy_analyzer on target, DefaultAzureCredential with Cognitive Services User role |
sample_get_result_file | Operation IDs, retrieving generated keyframes from video analysis |
[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
.env configuration, inactive venv, 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
-
First-time setup (run once per Foundry resource):
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh sample_update_defaults
-
Analyze a document from URL:
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh sample_analyze_url
-
Analyze a local PDF file:
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh sample_analyze_binary
-
Extract invoice fields:
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh sample_analyze_invoice
List Available Samples
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh --list
Scripts
This skill includes a single helper script in the scripts/ directory.
run_sample.sh — Run a sample (activates .venv and loads .env automatically)
A convenience wrapper that activates the virtual environment, sources .env, and runs the sample. Detects sync and async variants automatically.
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh sample_analyze_url
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh sample_analyze_invoice.py
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh sample_analyze_url_async
.github/skills/cu-sdk-sample-run/scripts/run_sample.sh --list
For environment setup (creating .venv, installing the SDK, writing .env), use the cu-sdk-setup skill's setup_user_env.sh / setup_user_env.ps1 script.
Troubleshooting
| Error | Solution |
|---|
ModuleNotFoundError: azure.ai.contentunderstanding | Activate venv: source .venv/bin/activate then pip install azure-ai-contentunderstanding |
ImportError: aiohttp package is not installed | Install dev dependencies: pip install -r dev_requirements.txt |
KeyError: 'CONTENTUNDERSTANDING_ENDPOINT' | Create .env file with credentials (see cu-sdk-setup skill) |
KeyError for any CONTENTUNDERSTANDING_TRAINING_DATA_* var (running sample_create_analyzer_with_labels) | Set either CONTENTUNDERSTANDING_TRAINING_DATA_SAS_URL (Option A) or both CONTENTUNDERSTANDING_TRAINING_DATA_STORAGE_ACCOUNT + CONTENTUNDERSTANDING_TRAINING_DATA_CONTAINER (Option B). See "Setting up sample_create_analyzer_with_labels training data" in Step 4. |
KeyError for any CONTENTUNDERSTANDING_TARGET_* or CONTENTUNDERSTANDING_SOURCE_* var (running sample_grant_copy_auth) | Configure all six cross-resource env vars. See "Setting up sample_grant_copy_auth cross-resource environment" in Step 4. |
FileNotFoundError: sample_files/... | Run samples from the samples/ directory (or use run_sample.sh, which sets the working directory automatically). |
Access denied or authorization errors (Foundry resource) | Ensure Cognitive Services User role is assigned; check API key or run az login. |
Access denied on Blob (running sample_create_analyzer_with_labels Option B) | Assign Storage Blob Data Contributor to the DefaultAzureCredential principal on the storage account, then re-run az login. |
SAS signature invalid / AuthenticationFailed (running sample_create_analyzer_with_labels Option A) | The SAS URL has expired or is missing Read + List permissions. Regenerate the SAS URL with both permissions. |
Model deployment not found | The sample requires sample_update_defaults.py to be run first. See the "Samples that require sample_update_defaults first" table in Step 4. Then run: cd samples && python sample_update_defaults.py. |
Model deployment '<name>' not found even after running sample_update_defaults | Your GPT_4_1_DEPLOYMENT / GPT_4_1_MINI_DEPLOYMENT / TEXT_EMBEDDING_3_LARGE_DEPLOYMENT env vars don't match the actual deployment names in your Foundry resource. Check the resource's deployments page and update .env. |
Related Skills
cu-sdk-setup - Set up environment for running samplescu-sdk-common-knowledge - Domain knowledge for Content Understanding concepts
Additional Resources
