azure-ai-document-intelligence-dotnet

작성자: microsoft

Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models. Use for invoice processing, receipt extraction, ID document analysis, and custom document models. Triggers: "Document Intelligence", "DocumentIntelligenceClient", "form recognizer", "invoice extraction", "receipt OCR", "document analysis .NET".

npx skills add https://github.com/microsoft/skills --skill azure-ai-document-intelligence-dotnet

Azure.AI.DocumentIntelligence (.NET)

Extract text, tables, and structured data from documents using prebuilt and custom models.

Installation

dotnet add package Azure.AI.DocumentIntelligence
dotnet add package Azure.Identity

Current Version: v1.0.0 (GA)

Environment Variables

DOCUMENT_INTELLIGENCE_ENDPOINT=https://<resource-name>.cognitiveservices.azure.com/  # Required: Document Intelligence endpoint
DOCUMENT_INTELLIGENCE_API_KEY=<your-api-key>  # Only required for AzureKeyCredential auth
BLOB_CONTAINER_SAS_URL=https://<storage>.blob.core.windows.net/<container>?<sas-token>  # Optional: blob container SAS URL for training data
AZURE_TOKEN_CREDENTIALS=prod  # Required only if DefaultAzureCredential is used in production

Authentication

Microsoft Entra Token Credential

using Azure.Identity;
using Azure.AI.DocumentIntelligence;

string endpoint = Environment.GetEnvironmentVariable("DOCUMENT_INTELLIGENCE_ENDPOINT");
// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
var credential = new DefaultAzureCredential(
    DefaultAzureCredential.DefaultEnvironmentVariableName
);
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/dotnet/api/overview/azure/identity-readme?view=azure-dotnet#credential-classes
// var credential = new ManagedIdentityCredential();
var client = new DocumentIntelligenceClient(new Uri(endpoint), credential);

Note: Entra ID requires a custom subdomain (e.g., https://<resource-name>.cognitiveservices.azure.com/), not a regional endpoint.

API Key

string endpoint = Environment.GetEnvironmentVariable("DOCUMENT_INTELLIGENCE_ENDPOINT");
string apiKey = Environment.GetEnvironmentVariable("DOCUMENT_INTELLIGENCE_API_KEY");
var client = new DocumentIntelligenceClient(new Uri(endpoint), new AzureKeyCredential(apiKey));

Client Types

ClientPurpose
DocumentIntelligenceClientAnalyze documents, classify documents
DocumentIntelligenceAdministrationClientBuild/manage custom models and classifiers

Prebuilt Models

Model IDDescription
prebuilt-readExtract text, languages, handwriting
prebuilt-layoutExtract text, tables, selection marks, structure
prebuilt-invoiceExtract invoice fields (vendor, items, totals)
prebuilt-receiptExtract receipt fields (merchant, items, total)
prebuilt-idDocumentExtract ID document fields (name, DOB, address)
prebuilt-businessCardExtract business card fields
prebuilt-tax.us.w2Extract W-2 tax form fields
prebuilt-healthInsuranceCard.usExtract health insurance card fields

Core Workflows

1. Analyze Invoice

using Azure.AI.DocumentIntelligence;

Uri invoiceUri = new Uri("https://example.com/invoice.pdf");

Operation<AnalyzeResult> operation = await client.AnalyzeDocumentAsync(
    WaitUntil.Completed, 
    "prebuilt-invoice", 
    invoiceUri);

AnalyzeResult result = operation.Value;

foreach (AnalyzedDocument document in result.Documents)
{
    if (document.Fields.TryGetValue("VendorName", out DocumentField vendorNameField)
        && vendorNameField.FieldType == DocumentFieldType.String)
    {
        string vendorName = vendorNameField.ValueString;
        Console.WriteLine($"Vendor Name: '{vendorName}', confidence: {vendorNameField.Confidence}");
    }

    if (document.Fields.TryGetValue("InvoiceTotal", out DocumentField invoiceTotalField)
        && invoiceTotalField.FieldType == DocumentFieldType.Currency)
    {
        CurrencyValue invoiceTotal = invoiceTotalField.ValueCurrency;
        Console.WriteLine($"Invoice Total: '{invoiceTotal.CurrencySymbol}{invoiceTotal.Amount}'");
    }
    
    // Extract line items
    if (document.Fields.TryGetValue("Items", out DocumentField itemsField)
        && itemsField.FieldType == DocumentFieldType.List)
    {
        foreach (DocumentField item in itemsField.ValueList)
        {
            var itemFields = item.ValueDictionary;
            if (itemFields.TryGetValue("Description", out DocumentField descField))
                Console.WriteLine($"  Item: {descField.ValueString}");
        }
    }
}

2. Extract Layout (Text, Tables, Structure)

Uri fileUri = new Uri("https://example.com/document.pdf");

Operation<AnalyzeResult> operation = await client.AnalyzeDocumentAsync(
    WaitUntil.Completed, 
    "prebuilt-layout", 
    fileUri);

AnalyzeResult result = operation.Value;

// Extract text by page
foreach (DocumentPage page in result.Pages)
{
    Console.WriteLine($"Page {page.PageNumber}: {page.Lines.Count} lines, {page.Words.Count} words");
    
    foreach (DocumentLine line in page.Lines)
    {
        Console.WriteLine($"  Line: '{line.Content}'");
    }
}

// Extract tables
foreach (DocumentTable table in result.Tables)
{
    Console.WriteLine($"Table: {table.RowCount} rows x {table.ColumnCount} columns");
    foreach (DocumentTableCell cell in table.Cells)
    {
        Console.WriteLine($"  Cell ({cell.RowIndex}, {cell.ColumnIndex}): {cell.Content}");
    }
}

3. Analyze Receipt

Operation<AnalyzeResult> operation = await client.AnalyzeDocumentAsync(
    WaitUntil.Completed, 
    "prebuilt-receipt", 
    receiptUri);

AnalyzeResult result = operation.Value;

foreach (AnalyzedDocument document in result.Documents)
{
    if (document.Fields.TryGetValue("MerchantName", out DocumentField merchantField))
        Console.WriteLine($"Merchant: {merchantField.ValueString}");
        
    if (document.Fields.TryGetValue("Total", out DocumentField totalField))
        Console.WriteLine($"Total: {totalField.ValueCurrency.Amount}");
        
    if (document.Fields.TryGetValue("TransactionDate", out DocumentField dateField))
        Console.WriteLine($"Date: {dateField.ValueDate}");
}

4. Build Custom Model

var adminClient = new DocumentIntelligenceAdministrationClient(
    new Uri(endpoint), 
    new AzureKeyCredential(apiKey));

string modelId = "my-custom-model";
Uri blobContainerUri = new Uri("<blob-container-sas-url>");

var blobSource = new BlobContentSource(blobContainerUri);
var options = new BuildDocumentModelOptions(modelId, DocumentBuildMode.Template, blobSource);

Operation<DocumentModelDetails> operation = await adminClient.BuildDocumentModelAsync(
    WaitUntil.Completed, 
    options);

DocumentModelDetails model = operation.Value;

Console.WriteLine($"Model ID: {model.ModelId}");
Console.WriteLine($"Created: {model.CreatedOn}");

foreach (var docType in model.DocumentTypes)
{
    Console.WriteLine($"Document type: {docType.Key}");
    foreach (var field in docType.Value.FieldSchema)
    {
        Console.WriteLine($"  Field: {field.Key}, Confidence: {docType.Value.FieldConfidence[field.Key]}");
    }
}

5. Build Document Classifier

string classifierId = "my-classifier";
Uri blobContainerUri = new Uri("<blob-container-sas-url>");

var sourceA = new BlobContentSource(blobContainerUri) { Prefix = "TypeA/train" };
var sourceB = new BlobContentSource(blobContainerUri) { Prefix = "TypeB/train" };

var docTypes = new Dictionary<string, ClassifierDocumentTypeDetails>()
{
    { "TypeA", new ClassifierDocumentTypeDetails(sourceA) },
    { "TypeB", new ClassifierDocumentTypeDetails(sourceB) }
};

var options = new BuildClassifierOptions(classifierId, docTypes);

Operation<DocumentClassifierDetails> operation = await adminClient.BuildClassifierAsync(
    WaitUntil.Completed, 
    options);

DocumentClassifierDetails classifier = operation.Value;
Console.WriteLine($"Classifier ID: {classifier.ClassifierId}");

6. Classify Document

string classifierId = "my-classifier";
Uri documentUri = new Uri("https://example.com/document.pdf");

var options = new ClassifyDocumentOptions(classifierId, documentUri);

Operation<AnalyzeResult> operation = await client.ClassifyDocumentAsync(
    WaitUntil.Completed, 
    options);

AnalyzeResult result = operation.Value;

foreach (AnalyzedDocument document in result.Documents)
{
    Console.WriteLine($"Document type: {document.DocumentType}, confidence: {document.Confidence}");
}

7. Manage Models

// Get resource details
DocumentIntelligenceResourceDetails resourceDetails = await adminClient.GetResourceDetailsAsync();
Console.WriteLine($"Custom models: {resourceDetails.CustomDocumentModels.Count}/{resourceDetails.CustomDocumentModels.Limit}");

// Get specific model
DocumentModelDetails model = await adminClient.GetModelAsync("my-model-id");
Console.WriteLine($"Model: {model.ModelId}, Created: {model.CreatedOn}");

// List models
await foreach (DocumentModelDetails modelItem in adminClient.GetModelsAsync())
{
    Console.WriteLine($"Model: {modelItem.ModelId}");
}

// Delete model
await adminClient.DeleteModelAsync("my-model-id");

Key Types Reference

TypeDescription
DocumentIntelligenceClientMain client for analysis
DocumentIntelligenceAdministrationClientModel management
AnalyzeResultResult of document analysis
AnalyzedDocumentSingle document within result
DocumentFieldExtracted field with value and confidence
DocumentFieldTypeString, Date, Number, Currency, etc.
DocumentPagePage info (lines, words, selection marks)
DocumentTableExtracted table with cells
DocumentModelDetailsCustom model metadata
BlobContentSourceTraining data source

Build Modes

ModeUse Case
DocumentBuildMode.TemplateFixed layout documents (forms)
DocumentBuildMode.NeuralVariable layout documents

Best Practices

  1. Use DefaultAzureCredential for production
  2. Reuse client instances — clients are thread-safe
  3. Handle long-running operations — Use WaitUntil.Completed for simplicity
  4. Check field confidence — Always verify Confidence property
  5. Use appropriate model — Prebuilt for common docs, custom for specialized
  6. Use custom subdomain — Required for Entra ID authentication

Error Handling

using Azure;

try
{
    var operation = await client.AnalyzeDocumentAsync(
        WaitUntil.Completed, 
        "prebuilt-invoice", 
        documentUri);
}
catch (RequestFailedException ex)
{
    Console.WriteLine($"Error: {ex.Status} - {ex.Message}");
}

Related SDKs

SDKPurposeInstall
Azure.AI.DocumentIntelligenceDocument analysis (this SDK)dotnet add package Azure.AI.DocumentIntelligence
Azure.AI.FormRecognizerLegacy SDK (deprecated)Use DocumentIntelligence instead

Reference Links

ResourceURL
NuGet Packagehttps://www.nuget.org/packages/Azure.AI.DocumentIntelligence
API Referencehttps://learn.microsoft.com/dotnet/api/azure.ai.documentintelligence
GitHub Sampleshttps://github.com/Azure/azure-sdk-for-net/tree/main/sdk/documentintelligence/Azure.AI.DocumentIntelligence/samples
Document Intelligence Studiohttps://documentintelligence.ai.azure.com/
Prebuilt Modelshttps://aka.ms/azsdk/formrecognizer/models

microsoft의 다른 스킬

oss-growth
microsoft
OSS 성장 해커 페르소나
agent-framework-azure-ai-py
microsoft
Microsoft Agent Framework Python SDK(agent-framework-azure-ai)를 사용하여 Azure AI Foundry 에이전트를 구축합니다. AzureAIAgentsProvider로 지속적 에이전트를 만들 때, 호스팅 도구(코드 인터프리터, 파일 검색, 웹 검색)를 사용할 때, MCP 서버를 통합할 때, 대화 스레드를 관리할 때, 또는 스트리밍 응답을 구현할 때 사용합니다. 함수 도구, 구조화된 출력, 다중 도구 에이전트를 다룹니다.
development
airunway-aks-setup
microsoft
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Azure Application Insights로 웹앱을 계측하기 위한 지침입니다. 원격 분석 패턴, SDK 설정, 구성 참조를 제공합니다. WHEN: 앱 계측 방법, App Insights SDK, 원격 분석 패턴, App Insights란 무엇인가, Application Insights 지침, 계측 예시, APM 모범 사례.
devops
applicationinsights-web-ts
microsoft
브라우저/웹 앱을 Application Insights JavaScript SDK(@microsoft/applicationinsights-web)로 계측합니다. Real User Monitoring(RUM) — 페이지 뷰, 클릭, AJAX/fetch 종속성, 예외, 사용자 지정 이벤트, 백엔드 OpenTelemetry 트레이스와 상관관계가 있는 브라우저 측 GenAI 에이전트 트레이스에 사용합니다. SDK Loader Script 및 npm 설정, 프레임워크 확장(React, React Native, Angular), Click Analytics, 텔레메트리 이니셜라이저, 브라우저에서 생성된 에이전트/도구/모델 스팬에 대한 OTel GenAI 의미론적 규칙을 다룹니다.
devops
azure-ai-anomalydetector-java
microsoft
Azure AI Anomaly Detector SDK for Java로 이상 탐지 애플리케이션을 구축하세요. 단변량/다변량 이상 탐지, 시계열 분석 또는 AI 기반 모니터링을 구현할 때 사용하세요.
development
azure-ai-language-conversations-py
microsoft
azure-ai-language-conversations Python SDK를 사용하여 대화형 언어 이해(CLU)를 구현합니다. ConversationAnalysisClient로 대화 의도와 엔터티를 분석하거나, NLP 기능을 구축하거나, 애플리케이션에 언어 이해를 통합할 때 사용합니다.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python. ML 작업 영역, 작업, 모델, 데이터 세트, 컴퓨팅 및 파이프라인에 사용합니다. 트리거: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
development