azure-document-intelligence

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.

> 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 | Lines | Description | |----------|-------|-------------| | Troubleshooting | L37-L42 | Diagnosing and fixing Document Intelligence latency problems, plus interpreting service error codes, causes, and recommended resolutions. | | Best Practices | L43-L53 | Improving custom model accuracy and confidence, labeling and table-tagging best practices, training/classification workflows, and managing the full Document Intelligence model lifecycle | | Decision Making | L54-L60 | Choosing 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 Patterns | L61-L65 | Guidance on designing disaster recovery, redundancy, and failover strategies for Azure AI Document Intelligence models and deployments. | | Limits & Quotas | L66-L75 | Quotas, rate limits, capacity add-ons, batch processing scale, and supported languages/locales for OCR, prebuilt, and custom Document Intelligence models. | | Security | L76-L83 | Securing Document Intelligence: creating SAS tokens, configuring data-at-rest encryption, and using managed identities and VNets to lock down access to resources. | | Configuration | L84-L89 | Configuring Document Intelligence containers and building, training, and composing custom models for tailored document processing workflows. | | Integrations & Coding Patterns | L90-L99 | Using 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 | | Deployment | L100-L106 | Deploying Document Intelligence via Docker/containers, including image tags, offline/disconnected setups, and installing/running the service and sample labeling tool. |

| Topic | URL | |-------|-----| | Troubleshoot Azure Document Intelligence latency issues | https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/concept/troubleshoot-latency?view=doc-intel-4.0.0 | | Interpret and resolve Document Intelligence error codes | https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/resolve-errors?view=doc-intel-4.0.0 |