Note
Form Recognizer is now Azure AI Document Intelligence!
- Code samples for each language's SDK are in the links below. The first step is to click to choose one (default JavaScript).
| Python | .NET | Java | JavaScript |
|---|
- The contents of this folder apply to the latest version: v4.0 (2024-02-29-preview) .
- You can click v3.1 (2023-07-31-GA) to view earlier versions.
Azure AI Document Intelligence is a cloud-based Azure AI service that enables you to build intelligent document processing solutions. Massive amounts of data, spanning a wide variety of data types, are stored in forms and documents. Document Intelligence enables you to effectively manage the velocity at which data is collected and processed and is key to improved operations, informed data-driven decisions, and enlightened innovation.
- Currently supported environments: LTS versions of Node.js
- Azure subscription - Create one for free.
Install the Azure DocumentIntelligence(formerlyFormRecognizer) REST client REST client library for JavaScript with npm:
npm install @azure-rest/ai-document-intelligenceTo use an Azure Active Directory (AAD) token credential, provide an instance of the desired credential type obtained from the @azure/identity library.
To authenticate with AAD, you must first npm install @azure/identity
After setup, you can choose which type of credential from @azure/identity to use.
As an example, DefaultAzureCredential
can be used to authenticate the client.
Set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET
import DocumentIntelligence from "@azure-rest/ai-document-intelligence";
const client = DocumentIntelligence(
process.env["DOCUMENT_INTELLIGENCE_ENDPOINT"],
new DefaultAzureCredential()
);import DocumentIntelligence from "@azure-rest/ai-document-intelligence";
const client = DocumentIntelligence(process.env["DOCUMENT_INTELLIGENCE_ENDPOINT"], {
key: process.env["DOCUMENT_INTELLIGENCE_API_KEY"],
});const initialResponse = await client
.path("/documentModels/{modelId}:analyze", "prebuilt-layout")
.post({
contentType: "application/json",
body: {
urlSource:
"https://raw.githubusercontent.com/Azure/azure-sdk-for-js/6704eff082aaaf2d97c1371a28461f512f8d748a/sdk/formrecognizer/ai-form-recognizer/assets/forms/Invoice_1.pdf",
},
queryParameters: { locale: "en-IN" },
});import fs from "fs";
import path from "path";
const filePath = path.join(ASSET_PATH, "forms", "Invoice_1.pdf");
const base64Source = fs.readFileSync(filePath, { encoding: "base64" });
const initialResponse = await client
.path("/documentModels/{modelId}:analyze", "prebuilt-layout")
.post({
contentType: "application/json",
body: {
base64Source,
},
queryParameters: { locale: "en-IN" },
});Continue creating the poller from initial response
import {
getLongRunningPoller,
AnalyzeResultOperationOutput,
isUnexpected,
} from "@azure-rest/ai-document-intelligence";
if (isUnexpected(initialResponse)) {
throw initialResponse.body.error;
}
const poller = await getLongRunningPoller(client, initialResponse);
const result = (await poller.pollUntilDone()).body as AnalyzeResultOperationOutput;
console.log(result);
// {
// status: 'succeeded',
// createdDateTime: '2023-11-10T13:31:31Z',
// lastUpdatedDateTime: '2023-11-10T13:31:34Z',
// analyzeResult: {
// apiVersion: '2023-10-31-preview',
// .
// .
// .
// contentFormat: 'text'
// }
// }Supports output with Markdown content format along with the default plain text. For now, this is only supported for "prebuilt-layout". Markdown content format is deemed a more friendly format for LLM consumption in a chat or automation use scenario.
Service follows the GFM spec (GitHub Flavored Markdown) for the Markdown format. Also introduces a new contentFormat property with value "text" or "markdown" to indicate the result content format.
import DocumentIntelligence from "@azure-rest/ai-document-intelligence";
const client = DocumentIntelligence(process.env["DOCUMENT_INTELLIGENCE_ENDPOINT"], {
key: process.env["DOCUMENT_INTELLIGENCE_API_KEY"],
});
const initialResponse = await client
.path("/documentModels/{modelId}:analyze", "prebuilt-layout")
.post({
contentType: "application/json",
body: {
urlSource:
"https://raw.githubusercontent.com/Azure/azure-sdk-for-js/6704eff082aaaf2d97c1371a28461f512f8d748a/sdk/formrecognizer/ai-form-recognizer/assets/forms/Invoice_1.pdf",
},
queryParameters: { outputContentFormat: "markdown" }, // <-- new query parameter
});When this feature flag is specified, the service will further extract the values of the fields specified via the queryFields query parameter to supplement any existing fields defined by the model as fallback.
await client.path("/documentModels/{modelId}:analyze", "prebuilt-layout").post({
contentType: "application/json",
body: { urlSource: "..." },
queryParameters: {
features: ["queryFields"],
queryFields: ["NumberOfGuests", "StoreNumber"],
}, // <-- new query parameter
});In the previous API versions supported by the older @azure/ai-form-recognizer library, document splitting and classification operation ("/documentClassifiers/{classifierId}:analyze") always tried to split the input file into multiple documents.
To enable a wider set of scenarios, service introduces a "split" query parameter with the new "2023-10-31-preview" service version. The following values are supported:
-
split: "auto"Let service determine where to split.
-
split: "none"The entire file is treated as a single document. No splitting is performed.
-
split: "perPage"Each page is treated as a separate document. Each empty page is kept as its own document.
Use the following examples to try more advanced features.
import {
DocumentClassifierBuildOperationDetailsOutput,
getLongRunningPoller,
isUnexpected,
} from "@azure-rest/ai-document-intelligence";
const containerSasUrl = (): string =>
process.env["DOCUMENT_INTELLIGENCE_TRAINING_CONTAINER_SAS_URL"];
const initialResponse = await client.path("/documentClassifiers:build").post({
body: {
classifierId: `customClassifier${getRandomNumber()}`,
description: "Custom classifier description",
docTypes: {
foo: {
azureBlobSource: {
containerUrl: containerSasUrl(),
},
},
bar: {
azureBlobSource: {
containerUrl: containerSasUrl(),
},
},
},
},
});
if (isUnexpected(initialResponse)) {
throw initialResponse.body.error;
}
const poller = await getLongRunningPoller(client, initialResponse);
const response = (await poller.pollUntilDone())
.body as DocumentClassifierBuildOperationDetailsOutput;
console.log(response);
// {
// operationId: '31466834048_f3ee629e-73fb-48ab-993b-1d55d73ca460',
// kind: 'documentClassifierBuild',
// status: 'succeeded',
// .
// .
// result: {
// classifierId: 'customClassifier10978',
// createdDateTime: '2023-11-09T12:45:56Z',
// .
// .
// description: 'Custom classifier description'
// },
// apiVersion: '2023-10-31-preview'
// }const response = await client.path("/info").get();
if (isUnexpected(response)) {
throw response.body.error;
}
console.log(response.body.customDocumentModels.limit);
// 20000import { paginate } from "@azure-rest/ai-document-intelligence";
const response = await client.path("/documentModels").get();
if (isUnexpected(response)) {
throw response.body.error;
}
const modelsInAccount: string[] = [];
for await (const model of paginate(client, response)) {
console.log(model.modelId);
}Enabling logging may help uncover useful information about failures. In order to see a log of HTTP requests and responses, set the AZURE_LOG_LEVEL environment variable to info. Alternatively, logging can be enabled at runtime by calling setLogLevel in the @azure/logger:
const { setLogLevel } = require("@azure/logger");
setLogLevel("info");- For more detailed instructions on how to enable logs, you can look at the @azure/logger package docs.
- For more examples, refer to: Azure SDK for JavaScript.