azure-ai-document-intelligence-ts

Azure Document Intelligence REST SDK for TypeScript

使用预构建模型和自定义模型，从文档中提取文本、表格和结构化数据。

安装

固定兼容的 REST SDK/API 版本，并确认目标区域支持所需模型。上传文档和通过 URL 进行分析会将内容披露给 Azure，也可能产生费用：仅使用经过授权的文档、短时有效且限定范围的 SAS，以及获批的保留设置。切勿记录提取出的 ID、医疗或银行数据，或 SAS URL。置信度分数并非经过校准的保证；请对照原始文档验证重要的总额和日期，并将不确定的结果提交审核。轮询时应设置截止时间或中止信号，并检查操作的终止状态；将响应强制转换为某种类型并不能证明操作成功。

npm install @azure-rest/ai-document-intelligence @azure/identity

环境变量

DOCUMENT_INTELLIGENCE_ENDPOINT=https://<resource>.cognitiveservices.azure.com
DOCUMENT_INTELLIGENCE_API_KEY=<api-key>

身份验证

重要：这是一个 REST 客户端。DocumentIntelligence 是一个函数，不是类。

DefaultAzureCredential

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

import DocumentIntelligence from "@azure-rest/ai-document-intelligence";

const client = DocumentIntelligence(
  process.env.DOCUMENT_INTELLIGENCE_ENDPOINT!,
  { key: process.env.DOCUMENT_INTELLIGENCE_API_KEY! }
);

分析文档 https://example.com

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);

分析文档（本地文件）

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;

预构建模型

模型 ID	说明
prebuilt-read	OCR - 文本和语言提取
prebuilt-layout	文本、表格、选择标记和结构
prebuilt-invoice	发票字段
prebuilt-receipt	收据字段
prebuilt-idDocument	身份证件字段
prebuilt-tax.us.w2	W-2 税表字段
prebuilt-healthInsuranceCard.us	医疗保险卡字段
prebuilt-contract	合同字段
prebuilt-bankStatement.us	银行对账单字段

提取发票字段

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);
}

提取收据字段

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);
  }
}

列出文档模型

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);
}

构建自定义模型

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);

构建文档分类器

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);

对文档进行分类

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);

获取服务信息

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);

轮询模式

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;

关键类型

import DocumentIntelligence, {
  isUnexpected,
  getLongRunningPoller,
  paginate,
  parseResultIdFromResponse,
  AnalyzeOperationOutput,
  DocumentClassifierBuildOperationDetailsOutput
} from "@azure-rest/ai-document-intelligence";

最佳实践

1. 使用 getLongRunningPoller() - 文档分析是异步的，请始终轮询结果
2. 检查 isUnexpected() - 用于正确处理错误的类型守卫
3. 选择合适的模型 - 尽可能使用预构建模型；针对特殊文档使用自定义模型
4. 处理置信度分数 - 字段带有置信度值，请根据使用场景设置阈值
5. 使用分页 - 使用 paginate() 辅助函数列出模型
6. 优先使用神经网络模式 - 对于自定义模型，神经网络模式比模板能处理更多变化

适用场景

当需要执行概述中描述的工作流或操作时，可使用此技能。

限制

• 仅当任务明确符合上述范围时，才使用此技能。
• 不要将输出视为环境特定验证、测试或专家审核的替代品。
• 如果缺少必要的输入、权限、安全边界或成功标准，请停止并请求澄清。