azure-document-intelligence

Expert knowledge for Azure AI Document Intelligence development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building, debugging, or optimizing Azure AI Document Intelligence applications. Not for Azure AI services (use azure-ai-services), Azure AI Search (use azure-cognitive-search), Azure AI Vision (use azure-ai-vision), Azure Machine Learning (use azure-machine-learning).

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Install skill "azure-document-intelligence" with this command: npx skills add microsoftdocs/agent-skills/microsoftdocs-agent-skills-azure-document-intelligence

Azure AI Document Intelligence Skill

This skill provides expert guidance for Azure AI Document Intelligence. Covers troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.

How to Use This Skill

IMPORTANT for Agent: This file may be large. Use the Category Index below to locate relevant sections, then use read_file with specific line ranges (e.g., L136-L144) to read the sections needed for the user's question

IMPORTANT for Agent: If metadata.generated_at is more than 3 months old, suggest the user pull the latest version from the repository. If mcp_microsoftdocs tools are not available, suggest the user install it: Installation Guide

This skill requires network access to fetch documentation content:

  • Preferred: Use mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.
  • Fallback: Use fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.

Category Index

CategoryLinesDescription
TroubleshootingL37-L42Diagnosing and fixing Document Intelligence latency problems, plus interpreting service error codes, causes, and recommended resolutions.
Best PracticesL43-L53Improving custom model accuracy and confidence, labeling and table-tagging best practices, training/classification workflows, and managing the full Document Intelligence model lifecycle
Decision MakingL54-L60Choosing the right Document Intelligence model, understanding version changes, and migrating or updating apps to API v3.1 based on changelog and migration guidance
Architecture & Design PatternsL61-L65Guidance on designing disaster recovery, redundancy, and failover strategies for Azure AI Document Intelligence models and deployments.
Limits & QuotasL66-L75Quotas, rate limits, capacity add-ons, batch processing scale, and supported languages/locales for OCR, prebuilt, and custom Document Intelligence models.
SecurityL76-L83Securing Document Intelligence: creating SAS tokens, configuring data-at-rest encryption, and using managed identities and VNets to lock down access to resources.
ConfigurationL84-L89Configuring Document Intelligence containers and building, training, and composing custom models for tailored document processing workflows.
Integrations & Coding PatternsL90-L99Using SDKs/REST to call Document Intelligence, handle AnalyzeDocument/Markdown outputs, and integrate with apps, Azure Functions, and Logic Apps for end‑to‑end document workflows
DeploymentL100-L106Deploying Document Intelligence via Docker/containers, including image tags, offline/disconnected setups, and installing/running the service and sample labeling tool.

Troubleshooting

Best Practices

Decision Making

Architecture & Design Patterns

Limits & Quotas

Security

Configuration

Integrations & Coding Patterns

Deployment

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