Azure Document Intelligence REST SDK for TypeScript
Extract text, tables, and structured data from documents using prebuilt and custom models.
Installation
Pin compatible REST SDK/API versions and confirm model availability in the target region. Document uploads and URL analysis disclose content to Azure and can incur charges: use only authorized documents, short-lived scoped SAS and approved retention settings. Never log extracted IDs, medical/bank data or SAS URLs. Confidence scores are not calibrated guarantees; validate material totals/dates against the original document and route uncertain results for review. Bound polling with a deadline/abort signal and inspect terminal operation status; casting a response type does not prove success.
bash
npm install @azure-rest/ai-document-intelligence @azure/identity
Environment Variables
bash
DOCUMENT_INTELLIGENCE_ENDPOINT=https://<resource>.cognitiveservices.azure.com
DOCUMENT_INTELLIGENCE_API_KEY=<api-key>
Authentication
Important: This is a REST client. DocumentIntelligence is a function, not a class.
DefaultAzureCredential
typescript
import DocumentIntelligence from "@azure-rest/ai-document-intelligence";
import { DefaultAzureCredential } from "@azure/identity";
const client = DocumentIntelligence(
process.env.DOCUMENT_INTELLIGENCE_ENDPOINT!,
new DefaultAzureCredential()
);
API Key
typescript
import DocumentIntelligence from "@azure-rest/ai-document-intelligence";
const client = DocumentIntelligence(
process.env.DOCUMENT_INTELLIGENCE_ENDPOINT!,
{ key: process.env.DOCUMENT_INTELLIGENCE_API_KEY! }
);
Analyze Document https://example.com
typescript
import DocumentIntelligence, {
isUnexpected,
getLongRunningPoller,
AnalyzeOperationOutput
} from "@azure-rest/ai-document-intelligence";
const initialResponse = await client
.path("/documentModels/{modelId}:analyze", "prebuilt-layout")
.post({
contentType: "application/json",
body: {
urlSource: "https://example.com/document.pdf"
},
queryParameters: { locale: "en-US" }
});
if (isUnexpected(initialResponse)) {
throw initialResponse.body.error;
}
const poller = getLongRunningPoller(client, initialResponse);
const result = (await poller.pollUntilDone()).body as AnalyzeOperationOutput;
console.log("Pages:", result.analyzeResult?.pages?.length);
console.log("Tables:", result.analyzeResult?.tables?.length);
Analyze Document (Local File)
typescript
import { readFile } from "node:fs/promises";
const fileBuffer = await readFile("./document.pdf");
const base64Source = fileBuffer.toString("base64");
const initialResponse = await client
.path("/documentModels/{modelId}:analyze", "prebuilt-invoice")
.post({
contentType: "application/json",
body: { base64Source }
});
if (isUnexpected(initialResponse)) {
throw initialResponse.body.error;
}
const poller = getLongRunningPoller(client, initialResponse);
const result = (await poller.pollUntilDone()).body as AnalyzeOperationOutput;
Prebuilt Models
| Model ID | Description | |
|---|
prebuilt-read | OCR - text and language extraction | |
prebuilt-layout | Text, tables, selection marks, structure | |
prebuilt-invoice | Invoice fields | |
prebuilt-receipt | Receipt fields | |
prebuilt-idDocument | ID document fields | |
prebuilt-tax.us.w2 | W-2 tax form fields | |
prebuilt-healthInsuranceCard.us | Health insurance card fields | |
prebuilt-contract | Contract fields | |
prebuilt-bankStatement.us | Bank statement fields | |
Extract Invoice Fields
typescript
const initialResponse = await client
.path("/documentModels/{modelId}:analyze", "prebuilt-invoice")
.post({
contentType: "application/json",
body: { urlSource: invoiceUrl }
});
if (isUnexpected(initialResponse)) {
throw initialResponse.body.error;
}
const poller = getLongRunningPoller(client, initialResponse);
const result = (await poller.pollUntilDone()).body as AnalyzeOperationOutput;
const invoice = result.analyzeResult?.documents?.[0];
if (invoice) {
console.log("Vendor:", invoice.fields?.VendorName?.content);
console.log("Total:", invoice.fields?.InvoiceTotal?.content);
console.log("Due Date:", invoice.fields?.DueDate?.content);
}
Extract Receipt Fields
typescript
const initialResponse = await client
.path("/documentModels/{modelId}:analyze", "prebuilt-receipt")
.post({
contentType: "application/json",
body: { urlSource: receiptUrl }
});
if (isUnexpected(initialResponse)) throw initialResponse.body.error;
const poller = getLongRunningPoller(client, initialResponse);
const result = (await poller.pollUntilDone()).body as AnalyzeOperationOutput;
const receipt = result.analyzeResult?.documents?.[0];
if (receipt) {
console.log("Merchant:", receipt.fields?.MerchantName?.content);
console.log("Total:", receipt.fields?.Total?.content);
for (const item of receipt.fields?.Items?.values || []) {
console.log("Item:", item.properties?.Description?.content);
console.log("Price:", item.properties?.TotalPrice?.content);
}
}
List Document Models
typescript
import DocumentIntelligence, { isUnexpected, paginate } from "@azure-rest/ai-document-intelligence";
const response = await client.path("/documentModels").get();
if (isUnexpected(response)) {
throw response.body.error;
}
for await (const model of paginate(client, response)) {
console.log(model.modelId);
}
Build Custom Model
typescript
const initialResponse = await client.path("/documentModels:build").post({
body: {
modelId: "my-custom-model",
description: "Custom model for purchase orders",
buildMode: "template", // or "neural"
azureBlobSource: {
containerUrl: process.env.TRAINING_CONTAINER_SAS_URL!,
prefix: "training-data/"
}
}
});
if (isUnexpected(initialResponse)) {
throw initialResponse.body.error;
}
const poller = getLongRunningPoller(client, initialResponse);
const result = await poller.pollUntilDone();
console.log("Model built:", result.body);
Build Document Classifier
typescript
import { DocumentClassifierBuildOperationDetailsOutput } from "@azure-rest/ai-document-intelligence";
const containerSasUrl = process.env.TRAINING_CONTAINER_SAS_URL!;
const initialResponse = await client.path("/documentClassifiers:build").post({
body: {
classifierId: "my-classifier",
description: "Invoice vs Receipt classifier",
docTypes: {
invoices: {
azureBlobSource: { containerUrl: containerSasUrl, prefix: "invoices/" }
},
receipts: {
azureBlobSource: { containerUrl: containerSasUrl, prefix: "receipts/" }
}
}
}
});
if (isUnexpected(initialResponse)) {
throw initialResponse.body.error;
}
const poller = getLongRunningPoller(client, initialResponse);
const result = (await poller.pollUntilDone()).body as DocumentClassifierBuildOperationDetailsOutput;
console.log("Classifier:", result.result?.classifierId);
Classify Document
typescript
const initialResponse = await client
.path("/documentClassifiers/{classifierId}:analyze", "my-classifier")
.post({
contentType: "application/json",
body: { urlSource: documentUrl },
queryParameters: { split: "auto" }
});
if (isUnexpected(initialResponse)) {
throw initialResponse.body.error;
}
const poller = getLongRunningPoller(client, initialResponse);
const result = await poller.pollUntilDone();
console.log("Classification:", result.body.analyzeResult?.documents);
Get Service Info
typescript
const response = await client.path("/info").get();
if (isUnexpected(response)) {
throw response.body.error;
}
console.log("Custom model limit:", response.body.customDocumentModels.limit);
console.log("Custom model count:", response.body.customDocumentModels.count);
Polling Pattern
typescript
import DocumentIntelligence, {
isUnexpected,
getLongRunningPoller,
AnalyzeOperationOutput
} from "@azure-rest/ai-document-intelligence";
// 1. Start operation
const initialResponse = await client
.path("/documentModels/{modelId}:analyze", "prebuilt-layout")
.post({ contentType: "application/json", body: { urlSource } });
// 2. Check for errors
if (isUnexpected(initialResponse)) {
throw initialResponse.body.error;
}
// 3. Create poller
const poller = getLongRunningPoller(client, initialResponse);
// 4. Optional: Monitor progress
poller.onProgress((state) => {
console.log("Status:", state.status);
});
// 5. Wait for completion
const result = (await poller.pollUntilDone()).body as AnalyzeOperationOutput;
Key Types
typescript
import DocumentIntelligence, {
isUnexpected,
getLongRunningPoller,
paginate,
parseResultIdFromResponse,
AnalyzeOperationOutput,
DocumentClassifierBuildOperationDetailsOutput
} from "@azure-rest/ai-document-intelligence";
Best Practices
- Use getLongRunningPoller() - Document analysis is async, always poll for results
- Check isUnexpected() - Type guard for proper error handling
- Choose the right model - Use prebuilt models when possible, custom for specialized docs
- Handle confidence scores - Fields have confidence values, set thresholds for your use case
- Use pagination - Use
paginate() helper for listing models - Prefer neural mode - For custom models, neural handles more variation than template
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.