/azure-document-intelligence

name: azure-document-intelligence
description: Expert knowledge for Azure AI Document Intelligence development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when training custom models, composing templates, using REST/SDK APIs, deploying containers, or planning v4.0 upgrades, and other Azure AI Document Intelligence related development tasks. Not for Azure AI Search (use azure-cognitive-search), Azure AI Custom Vision (use azure-custom-vision), Azure AI Language (use azure-language-service), Azure AI Video Indexer (use azure-video-indexer).
compatibility: Requires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or fetch_webpage to retrieve documentation.
metadata:
  generated_at: "2026-07-12"
  generator: "docs2skills/1.0.0"

Azure AI Document Intelligence Skill

This skill provides expert guidance for Azure AI Document Intelligence. Covers troubleshooting, best practices, decision making, 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**: Use the **Category Index** below to locate relevant sections. For categories with line ranges (e.g., `L35-L120`), use `read_file` with the specified lines. For categories with file links (e.g., `[security.md](security.md)`), use `read_file` on the linked reference file

> **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](https://github.com/MicrosoftDocs/mcp/blob/main/README.md)

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

| Category | Lines | Description | |----------|-------|-------------| | Troubleshooting | L36-L42 | Diagnosing latency, understanding and fixing Document Intelligence API error codes, and handling known service issues and limitations. | | Best Practices | L43-L54 | Guidance on training, labeling, composing, and managing custom/classification/template models to maximize Document Intelligence accuracy, confidence, and lifecycle quality. | | Decision Making | L55-L61 | Guidance on choosing the right Document Intelligence model, estimating usage/costs, and planning migration and version upgrades (including to v4.0). | | Limits & Quotas | L62-L72 | Capacity add-ons, container image tags, OCR and model language/locale support, batch processing at scale, and service quotas/limits for Azure Document Intelligence. | | Security | L73-L80 | Securing Document Intelligence resources: creating SAS tokens, configuring data-at-rest encryption with customer-managed keys, and using managed identities and VNETs for secure access. | | Configuration | L81-L86 | Configuring and deploying Document Intelligence containers, and managing/sharing custom model projects in Document Intelligence Studio for collaborative use. | | Integrations & Coding Patterns | L87-L97 | How to call Document Intelligence via REST/SDKs, use the sample tool, and integrate outputs (JSON/Markdown) into workflows with Azure Functions and Logic Apps. | | Deployment | L98-L105 | Guides for deploying Document Intelligence: Docker/container setup (including offline), SDK/REST API usage, disaster recovery, and deploying the sample labeling tool. |

Troubleshooting

| 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 | | Reference and resolve Document Intelligence API errors | https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/resolve-errors?view=doc-intel-4.0.0 | | Handle known issues in Azure Document Intelligence | https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/reference/known-issues?view=doc-intel-4.0.0 |

Best Practices

| Topic | URL | |-------|-----| | Improve Document Intelligence accuracy and confidence | https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/concept/accuracy-confidence?view=doc-intel-4.0.0 | | Train custom document classification models | https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/build-a-custom-classifier?view=doc-intel-4.0.0 | | Build and train custom Document Intelligence models | https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/build-a-custom-model?view=doc-intel-4.0.0 | | Create and compose custom Document Intelligence models | https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/compose-custom-models?view=doc-intel-4.0.0 | | Use efficient labeling tips in Document Intelligence Studio | https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/train/custom-label-tips?view=doc-intel-4.0.0 | | Apply labeling best practices for high-accuracy custom models | https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/train/custom-labels?view=doc-intel-4.0.0 | | Manage Azure Document Intelligence custom model lifecycle | https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/train/custom-lifecycle?view=doc-intel-4.0.0 | | Train template models using supervised table tags | https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/v21/supervised-table-tags?view=doc-intel-2.1.0 |

Decision Making

| Topic | URL | |-------|-----| | Select the right Azure Docum