DocumentAnalysisClient class

套件:: @azure/ai-form-recognizer

用來與窗體辨識器服務分析功能互動的用戶端。

例子：

表單辨識器服務和用戶端支援兩種驗證方式：

Azure Active Directory

import { DefaultAzureCredential } from "@azure/identity";
import { DocumentAnalysisClient } from "@azure/ai-form-recognizer";

const credential = new DefaultAzureCredential();
const client = new DocumentAnalysisClient(
  "https://<resource name>.cognitiveservices.azure.com",
  credential,
);

API 金鑰（訂用帳戶金鑰）

import { AzureKeyCredential, DocumentAnalysisClient } from "@azure/ai-form-recognizer";

const credential = new AzureKeyCredential("<API key>");
const client = new DocumentAnalysisClient(
  "https://<resource name>.cognitiveservices.azure.com",
  credential,
);

建構函式

DocumentAnalysisClient(string, KeyCredential, DocumentAnalysisClientOptions)

從資源端點和靜態 API 金鑰建立 DocumentAnalysisClient 實例（KeyCredential），

例：

import { AzureKeyCredential, DocumentAnalysisClient } from "@azure/ai-form-recognizer";

const credential = new AzureKeyCredential("<API key>");
const client = new DocumentAnalysisClient(
  "https://<resource name>.cognitiveservices.azure.com",
  credential,
);

DocumentAnalysisClient(string, TokenCredential, DocumentAnalysisClientOptions)

從資源端點和 Azure 身分識別 DocumentAnalysisClient建立 TokenCredential 實例。

如需使用 Azure Active Directory 進行驗證的詳細資訊，請參閱 @azure/identity 套件。

例：

import { DefaultAzureCredential } from "@azure/identity";
import { DocumentAnalysisClient } from "@azure/ai-form-recognizer";

const credential = new DefaultAzureCredential();
const client = new DocumentAnalysisClient(
  "https://<resource name>.cognitiveservices.azure.com",
  credential,
);

方法

beginAnalyzeDocument(string, FormRecognizerRequestBody, AnalyzeDocumentOptions<AnalyzeResult<AnalyzedDocument>>)	使用其唯一標識碼所提供的模型，從輸入擷取數據。此作業支援自定義和預先建置的模型。例如，若要使用預先建置的發票模型，請提供模型標識碼「prebuilt-invoice」，或使用更簡單的預先建置版面配置模型，提供模型標識符「prebuilt-layout」。。 `AnalyzeResult` 中產生的欄位取決於用於分析的模型，而所擷取檔欄位中的值取決於模型中的檔類型（如果有的話），以及其對應的欄位架構。例子此方法支援可串流要求主體（FormRecognizerRequestBody），例如Node.JS `ReadableStream`对象、瀏覽器 `Blob`和 `ArrayBuffer`s。本文的內容將會上傳至服務進行分析。 import { DefaultAzureCredential } from "@azure/identity"; import { DocumentAnalysisClient } from "@azure/ai-form-recognizer"; import { createReadStream } from "node:fs"; import { PrebuiltReceiptModel } from "../samples-dev/prebuilt/prebuilt-receipt.js"; const credential = new DefaultAzureCredential(); const client = new DocumentAnalysisClient( "https://<resource name>.cognitiveservices.azure.com", credential, ); const path = "<path to a document>"; const readStream = createReadStream(path); // The PrebuiltReceiptModel `DocumentModel` instance encodes both the model ID and a stronger return type for the operation const poller = await client.beginAnalyzeDocument(PrebuiltReceiptModel, readStream, { onProgress: ({ status }) => { console.log(`status: ${status}`); }, }); const { documents: [receiptDocument], } = await poller.pollUntilDone(); // The fields of the document constitute the extracted receipt data. const receipt = receiptDocument.fields; if (receipt === undefined) { throw new Error("Expected at least one receipt in analysis result."); } console.log(`Receipt data (${receiptDocument.docType})`); console.log(" Merchant Name:", receipt.merchantName?.value); // The items of the receipt are an example of a `DocumentArrayValue` if (receipt.items !== undefined) { console.log("Items:"); for (const { properties: item } of receipt.items.values) { console.log("- Description:", item.description?.value); console.log(" Total Price:", item.totalPrice?.value); } } console.log(" Total:", receipt.total?.value);
beginAnalyzeDocument<Result>(DocumentModel<Result>, FormRecognizerRequestBody, AnalyzeDocumentOptions<Result>)	使用具有已知強型別文件架構的模型從輸入擷取數據（DocumentModel）。 `AnalyzeResult` 中產生的欄位取決於用於分析的模型。在 TypeScript 中，這個方法多載的結果類型是從輸入的類型推斷 `DocumentModel`。例子此方法支援可串流要求主體（FormRecognizerRequestBody），例如Node.JS `ReadableStream`对象、瀏覽器 `Blob`和 `ArrayBuffer`s。本文的內容將會上傳至服務進行分析。如果提供的輸入是字串，則會將它視為要分析之檔位置的URL。如需詳細資訊，請參閱 beginAnalyzeDocumentFromUrl 方法。使用 URL 時，最好使用該方法，而且只有在此方法中才提供 URL 支援，以提供回溯相容性。 import { DefaultAzureCredential } from "@azure/identity"; import { DocumentAnalysisClient } from "@azure/ai-form-recognizer"; import { createReadStream } from "node:fs"; import { PrebuiltReceiptModel } from "../samples-dev/prebuilt/prebuilt-receipt.js"; const credential = new DefaultAzureCredential(); const client = new DocumentAnalysisClient( "https://<resource name>.cognitiveservices.azure.com", credential, ); const path = "<path to a document>"; const readStream = createReadStream(path); // The PrebuiltReceiptModel `DocumentModel` instance encodes both the model ID and a stronger return type for the operation const poller = await client.beginAnalyzeDocument(PrebuiltReceiptModel, readStream, { onProgress: ({ status }) => { console.log(`status: ${status}`); }, }); const { documents: [receiptDocument], } = await poller.pollUntilDone(); // The fields of the document constitute the extracted receipt data. const receipt = receiptDocument.fields; if (receipt === undefined) { throw new Error("Expected at least one receipt in analysis result."); } console.log(`Receipt data (${receiptDocument.docType})`); console.log(" Merchant Name:", receipt.merchantName?.value); // The items of the receipt are an example of a `DocumentArrayValue` if (receipt.items !== undefined) { console.log("Items:"); for (const { properties: item } of receipt.items.values) { console.log("- Description:", item.description?.value); console.log(" Total Price:", item.totalPrice?.value); } } console.log(" Total:", receipt.total?.value);
beginAnalyzeDocumentFromUrl(string, string, AnalyzeDocumentOptions<AnalyzeResult<AnalyzedDocument>>)	使用其唯一標識碼所提供的模型，從輸入擷取數據。此作業支援自定義和預先建置的模型。例如，若要使用預先建置的發票模型，請提供模型標識碼「prebuilt-invoice」，或使用更簡單的預先建置版面配置模型，提供模型標識符「prebuilt-layout」。。 `AnalyzeResult` 中產生的欄位取決於用於分析的模型，而所擷取檔欄位中的值取決於模型中的檔類型（如果有的話），以及其對應的欄位架構。例子這個方法支援從指定 URL 的檔案擷取數據。窗體辨識器服務會嘗試使用提交的 URL 下載檔案，因此必須可從公用因特網存取該 URL。例如，SAS 令牌可用來授與 Azure 記憶體中 Blob 的讀取許可權，而服務會使用 SAS 編碼的 URL 來要求檔案。 import { DefaultAzureCredential } from "@azure/identity"; import { DocumentAnalysisClient, DocumentStringField, DocumentArrayField, DocumentObjectField, } from "@azure/ai-form-recognizer"; const credential = new DefaultAzureCredential(); const client = new DocumentAnalysisClient( "https://<resource name>.cognitiveservices.azure.com", credential, ); const poller = await client.beginAnalyzeDocumentFromUrl( "prebuilt-receipt", // The Document Intelligence service will access the following URL to a receipt image and extract data from it "https://raw.githubusercontent.com/Azure/azure-sdk-for-js/main/sdk/formrecognizer/ai-form-recognizer/assets/receipt/contoso-receipt.png", ); poller.onProgress((state) => console.log("Operation:", state.modelId, state.status)); const { documents } = await poller.pollUntilDone(); const result = documents && documents[0]; if (result) { const receipt = result.fields; console.log("=== Receipt Information ==="); console.log("Type:", result.docType); console.log("Merchant:", (receipt["MerchantName"] as DocumentStringField).value); console.log("Items:"); for (const { properties: item } of ((receipt["Items"] as DocumentArrayField).values \|\| []) as DocumentObjectField[]) { console.log("- Description:", (item["Description"] as DocumentStringField).value); console.log(" Total Price:", (item["TotalPrice"] as DocumentStringField).value); } } else { throw new Error("Expected at least one receipt in the result."); }
beginAnalyzeDocumentFromUrl<Result>(DocumentModel<Result>, string, AnalyzeDocumentOptions<Result>)	使用具有已知強型別文件架構的模型從輸入擷取數據（DocumentModel）。 `AnalyzeResult` 中產生的欄位取決於用於分析的模型。在 TypeScript 中，這個方法多載的結果類型是從輸入的類型推斷 `DocumentModel`。例子這個方法支援從指定 URL 的檔案擷取數據。窗體辨識器服務會嘗試使用提交的 URL 下載檔案，因此必須可從公用因特網存取該 URL。例如，SAS 令牌可用來授與 Azure 記憶體中 Blob 的讀取許可權，而服務會使用 SAS 編碼的 URL 來要求檔案。 import { DefaultAzureCredential } from "@azure/identity"; import { DocumentAnalysisClient } from "@azure/ai-form-recognizer"; import { PrebuiltReceiptModel } from "../samples-dev/prebuilt/prebuilt-receipt.js"; const credential = new DefaultAzureCredential(); const client = new DocumentAnalysisClient( "https://<resource name>.cognitiveservices.azure.com", credential, ); const poller = await client.beginAnalyzeDocumentFromUrl( PrebuiltReceiptModel, // The Document Intelligence service will access the following URL to a receipt image and extract data from it "https://raw.githubusercontent.com/Azure/azure-sdk-for-js/main/sdk/formrecognizer/ai-form-recognizer/assets/receipt/contoso-receipt.png", ); const { documents: [document], } = await poller.pollUntilDone(); // Use of PrebuiltModels.Receipt above (rather than the raw model ID), as it adds strong typing of the model's output if (document) { const { merchantName, items, total } = document.fields; console.log("=== Receipt Information ==="); console.log("Type:", document.docType); console.log("Merchant:", merchantName && merchantName.value); console.log("Items:"); for (const item of (items && items.values) \|\| []) { const { description, totalPrice } = item.properties; console.log("- Description:", description && description.value); console.log(" Total Price:", totalPrice && totalPrice.value); } console.log("Total:", total && total.value); } else { throw new Error("Expected at least one receipt in the result."); }
beginClassifyDocument(string, FormRecognizerRequestBody, ClassifyDocumentOptions)	使用識別碼所指定的自定義分類器來分類檔。這個方法會產生長時間執行的作業（poller），最終會產生 `AnalyzeResult`。這與 `beginAnalyzeDocument` 和 `beginAnalyzeDocumentFromUrl`相同，但結果只會包含其字段的小子集。只會填入 [`documents`] 字段和 [`pages`] 字段，而且只會傳回最少的頁面資訊。 [`documents`] 字段將包含所有已識別檔及其分類為 `docType` 的相關信息。例此方法支援可串流要求主體（FormRecognizerRequestBody），例如Node.JS `ReadableStream`对象、瀏覽器 `Blob`和 `ArrayBuffer`s。本文的內容將會上傳至服務進行分析。 import { DefaultAzureCredential } from "@azure/identity"; import { DocumentAnalysisClient } from "@azure/ai-form-recognizer"; import { createReadStream } from "node:fs"; const credential = new DefaultAzureCredential(); const client = new DocumentAnalysisClient( "https://<resource name>.cognitiveservices.azure.com", credential, ); const path = "<path to a document>"; const readStream = createReadStream(path); const poller = await client.beginClassifyDocument("<classifier id>", readStream); const result = await poller.pollUntilDone(); if (result?.documents?.length === 0) { throw new Error("Failed to extract any documents."); } for (const document of result.documents) { console.log( `Extracted a document with type '${document.docType}' on page ${document.boundingRegions?.[0].pageNumber} (confidence: ${document.confidence})`, ); }
beginClassifyDocumentFromUrl(string, string, ClassifyDocumentOptions)	使用標識碼所提供的自定義分類器，從 URL 分類檔。這個方法會產生長時間執行的作業（poller），最終會產生 `AnalyzeResult`。這與 `beginAnalyzeDocument` 和 `beginAnalyzeDocumentFromUrl`相同，但結果只會包含其字段的小子集。只會填入 [`documents`] 字段和 [`pages`] 字段，而且只會傳回最少的頁面資訊。 [`documents`] 字段將包含所有已識別檔及其分類為 `docType` 的相關信息。例這個方法支援從指定 URL 的檔案擷取數據。窗體辨識器服務會嘗試使用提交的 URL 下載檔案，因此必須可從公用因特網存取該 URL。例如，SAS 令牌可用來授與 Azure 記憶體中 Blob 的讀取許可權，而服務會使用 SAS 編碼的 URL 來要求檔案。 import { DefaultAzureCredential } from "@azure/identity"; import { DocumentAnalysisClient } from "@azure/ai-form-recognizer"; const credential = new DefaultAzureCredential(); const client = new DocumentAnalysisClient( "https://<resource name>.cognitiveservices.azure.com", credential, ); const documentUrl = "https://raw.githubusercontent.com/Azure/azure-sdk-for-js/main/sdk/formrecognizer/ai-form-recognizer/assets/invoice/Invoice_1.pdf"; const poller = await client.beginClassifyDocumentFromUrl("<classifier id>", documentUrl); const result = await poller.pollUntilDone(); if (result?.documents?.length === 0) { throw new Error("Failed to extract any documents."); } for (const document of result.documents) { console.log( `Extracted a document with type '${document.docType}' on page ${document.boundingRegions?.[0].pageNumber} (confidence: ${document.confidence})`, ); }

建構函式詳細資料

DocumentAnalysisClient(string, KeyCredential, DocumentAnalysisClientOptions)

從資源端點和靜態 API 金鑰建立 DocumentAnalysisClient 實例（KeyCredential），

例：

import { AzureKeyCredential, DocumentAnalysisClient } from "@azure/ai-form-recognizer";

const credential = new AzureKeyCredential("<API key>");
const client = new DocumentAnalysisClient(
  "https://<resource name>.cognitiveservices.azure.com",
  credential,
);

new DocumentAnalysisClient(endpoint: string, credential: KeyCredential, options?: DocumentAnalysisClientOptions)

參數

endpoint: string

Azure 認知服務實例的端點 URL

credential: KeyCredential

包含認知服務實例訂用帳戶密鑰的 KeyCredential

options: DocumentAnalysisClientOptions

在客戶端中設定所有方法的選擇性設定

DocumentAnalysisClient(string, TokenCredential, DocumentAnalysisClientOptions)

從資源端點和 Azure 身分識別 DocumentAnalysisClient建立 TokenCredential 實例。

如需使用 Azure Active Directory 進行驗證的詳細資訊，請參閱 @azure/identity 套件。

例：

import { DefaultAzureCredential } from "@azure/identity";
import { DocumentAnalysisClient } from "@azure/ai-form-recognizer";

const credential = new DefaultAzureCredential();
const client = new DocumentAnalysisClient(
  "https://<resource name>.cognitiveservices.azure.com",
  credential,
);

new DocumentAnalysisClient(endpoint: string, credential: TokenCredential, options?: DocumentAnalysisClientOptions)

參數

endpoint: string

Azure 認知服務實例的端點 URL

credential: TokenCredential

來自 @azure/identity 套件的 TokenCredential 實例

options: DocumentAnalysisClientOptions

在客戶端中設定所有方法的選擇性設定

方法詳細資料

beginAnalyzeDocument(string, FormRecognizerRequestBody, AnalyzeDocumentOptions<AnalyzeResult<AnalyzedDocument>>)

使用其唯一標識碼所提供的模型，從輸入擷取數據。

此作業支援自定義和預先建置的模型。例如，若要使用預先建置的發票模型，請提供模型標識碼「prebuilt-invoice」，或使用更簡單的預先建置版面配置模型，提供模型標識符「prebuilt-layout」。。

AnalyzeResult 中產生的欄位取決於用於分析的模型，而所擷取檔欄位中的值取決於模型中的檔類型（如果有的話），以及其對應的欄位架構。

例子

此方法支援可串流要求主體（FormRecognizerRequestBody），例如Node.JS ReadableStream对象、瀏覽器 Blob和 ArrayBuffers。本文的內容將會上傳至服務進行分析。

import { DefaultAzureCredential } from "@azure/identity";
import { DocumentAnalysisClient } from "@azure/ai-form-recognizer";
import { createReadStream } from "node:fs";
import { PrebuiltReceiptModel } from "../samples-dev/prebuilt/prebuilt-receipt.js";

const credential = new DefaultAzureCredential();
const client = new DocumentAnalysisClient(
  "https://<resource name>.cognitiveservices.azure.com",
  credential,
);

const path = "<path to a document>";
const readStream = createReadStream(path);

// The PrebuiltReceiptModel `DocumentModel` instance encodes both the model ID and a stronger return type for the operation
const poller = await client.beginAnalyzeDocument(PrebuiltReceiptModel, readStream, {
  onProgress: ({ status }) => {
    console.log(`status: ${status}`);
  },
});

const {
  documents: [receiptDocument],
} = await poller.pollUntilDone();

// The fields of the document constitute the extracted receipt data.
const receipt = receiptDocument.fields;

if (receipt === undefined) {
  throw new Error("Expected at least one receipt in analysis result.");
}

console.log(`Receipt data (${receiptDocument.docType})`);
console.log("  Merchant Name:", receipt.merchantName?.value);

// The items of the receipt are an example of a `DocumentArrayValue`
if (receipt.items !== undefined) {
  console.log("Items:");
  for (const { properties: item } of receipt.items.values) {
    console.log("- Description:", item.description?.value);
    console.log("  Total Price:", item.totalPrice?.value);
  }
}

console.log("  Total:", receipt.total?.value);

function beginAnalyzeDocument(modelId: string, document: FormRecognizerRequestBody, options?: AnalyzeDocumentOptions<AnalyzeResult<AnalyzedDocument>>): Promise<AnalysisPoller<AnalyzeResult<AnalyzedDocument>>>

參數

modelId: string

這個客戶端資源內模型的唯一識別元（名稱）

document: FormRecognizerRequestBody

將隨要求上傳的 FormRecognizerRequestBody

options: AnalyzeDocumentOptions<AnalyzeResult<AnalyzedDocument>>

分析作業和輪詢器選擇性設定

傳回

Promise<AnalysisPoller<AnalyzeResult<AnalyzedDocument>>>

長時間執行的作業（投票器）最終會產生 AnalyzeResult

beginAnalyzeDocument<Result>(DocumentModel<Result>, FormRecognizerRequestBody, AnalyzeDocumentOptions<Result>)

使用具有已知強型別文件架構的模型從輸入擷取數據（DocumentModel）。

AnalyzeResult 中產生的欄位取決於用於分析的模型。在 TypeScript 中，這個方法多載的結果類型是從輸入的類型推斷 DocumentModel。

例子

此方法支援可串流要求主體（FormRecognizerRequestBody），例如Node.JS ReadableStream对象、瀏覽器 Blob和 ArrayBuffers。本文的內容將會上傳至服務進行分析。

如果提供的輸入是字串，則會將它視為要分析之檔位置的URL。如需詳細資訊，請參閱 beginAnalyzeDocumentFromUrl 方法。使用 URL 時，最好使用該方法，而且只有在此方法中才提供 URL 支援，以提供回溯相容性。

import { DefaultAzureCredential } from "@azure/identity";
import { DocumentAnalysisClient } from "@azure/ai-form-recognizer";
import { createReadStream } from "node:fs";
import { PrebuiltReceiptModel } from "../samples-dev/prebuilt/prebuilt-receipt.js";

const credential = new DefaultAzureCredential();
const client = new DocumentAnalysisClient(
  "https://<resource name>.cognitiveservices.azure.com",
  credential,
);

const path = "<path to a document>";
const readStream = createReadStream(path);

// The PrebuiltReceiptModel `DocumentModel` instance encodes both the model ID and a stronger return type for the operation
const poller = await client.beginAnalyzeDocument(PrebuiltReceiptModel, readStream, {
  onProgress: ({ status }) => {
    console.log(`status: ${status}`);
  },
});

const {
  documents: [receiptDocument],
} = await poller.pollUntilDone();

// The fields of the document constitute the extracted receipt data.
const receipt = receiptDocument.fields;

if (receipt === undefined) {
  throw new Error("Expected at least one receipt in analysis result.");
}

console.log(`Receipt data (${receiptDocument.docType})`);
console.log("  Merchant Name:", receipt.merchantName?.value);

// The items of the receipt are an example of a `DocumentArrayValue`
if (receipt.items !== undefined) {
  console.log("Items:");
  for (const { properties: item } of receipt.items.values) {
    console.log("- Description:", item.description?.value);
    console.log("  Total Price:", item.totalPrice?.value);
  }
}

console.log("  Total:", receipt.total?.value);

function beginAnalyzeDocument<Result>(model: DocumentModel<Result>, document: FormRecognizerRequestBody, options?: AnalyzeDocumentOptions<Result>): Promise<AnalysisPoller<Result>>

參數

model: DocumentModel<Result>

DocumentModel，代表要用於分析和預期輸出類型的模型

document: FormRecognizerRequestBody

將隨要求上傳的 FormRecognizerRequestBody

options: AnalyzeDocumentOptions<Result>

分析作業和輪詢器選擇性設定

傳回

Promise<AnalysisPoller<Result>>

長時間執行的作業（輪詢器）最終會產生與輸入模型相關聯結果類型的檔 AnalyzeResult

beginAnalyzeDocumentFromUrl(string, string, AnalyzeDocumentOptions<AnalyzeResult<AnalyzedDocument>>)

使用其唯一標識碼所提供的模型，從輸入擷取數據。

AnalyzeResult 中產生的欄位取決於用於分析的模型，而所擷取檔欄位中的值取決於模型中的檔類型（如果有的話），以及其對應的欄位架構。

例子

這個方法支援從指定 URL 的檔案擷取數據。窗體辨識器服務會嘗試使用提交的 URL 下載檔案，因此必須可從公用因特網存取該 URL。例如，SAS 令牌可用來授與 Azure 記憶體中 Blob 的讀取許可權，而服務會使用 SAS 編碼的 URL 來要求檔案。

import { DefaultAzureCredential } from "@azure/identity";
import {
  DocumentAnalysisClient,
  DocumentStringField,
  DocumentArrayField,
  DocumentObjectField,
} from "@azure/ai-form-recognizer";

const credential = new DefaultAzureCredential();
const client = new DocumentAnalysisClient(
  "https://<resource name>.cognitiveservices.azure.com",
  credential,
);

const poller = await client.beginAnalyzeDocumentFromUrl(
  "prebuilt-receipt",
  // The Document Intelligence service will access the following URL to a receipt image and extract data from it
  "https://raw.githubusercontent.com/Azure/azure-sdk-for-js/main/sdk/formrecognizer/ai-form-recognizer/assets/receipt/contoso-receipt.png",
);
poller.onProgress((state) => console.log("Operation:", state.modelId, state.status));

const { documents } = await poller.pollUntilDone();

const result = documents && documents[0];
if (result) {
  const receipt = result.fields;
  console.log("=== Receipt Information ===");
  console.log("Type:", result.docType);
  console.log("Merchant:", (receipt["MerchantName"] as DocumentStringField).value);

  console.log("Items:");
  for (const { properties: item } of ((receipt["Items"] as DocumentArrayField).values ||
    []) as DocumentObjectField[]) {
    console.log("- Description:", (item["Description"] as DocumentStringField).value);
    console.log("  Total Price:", (item["TotalPrice"] as DocumentStringField).value);
  }
} else {
  throw new Error("Expected at least one receipt in the result.");
}

function beginAnalyzeDocumentFromUrl(modelId: string, documentUrl: string, options?: AnalyzeDocumentOptions<AnalyzeResult<AnalyzedDocument>>): Promise<AnalysisPoller<AnalyzeResult<AnalyzedDocument>>>

參數

modelId: string

這個客戶端資源內模型的唯一識別元（名稱）

documentUrl: string

可從公用因特網存取之輸入檔案的網址（字串）

options: AnalyzeDocumentOptions<AnalyzeResult<AnalyzedDocument>>

分析作業和輪詢器選擇性設定

傳回

Promise<AnalysisPoller<AnalyzeResult<AnalyzedDocument>>>

長時間執行的作業（投票器）最終會產生 AnalyzeResult

beginAnalyzeDocumentFromUrl<Result>(DocumentModel<Result>, string, AnalyzeDocumentOptions<Result>)

使用具有已知強型別文件架構的模型從輸入擷取數據（DocumentModel）。

AnalyzeResult 中產生的欄位取決於用於分析的模型。在 TypeScript 中，這個方法多載的結果類型是從輸入的類型推斷 DocumentModel。

例子

import { DefaultAzureCredential } from "@azure/identity";
import { DocumentAnalysisClient } from "@azure/ai-form-recognizer";
import { PrebuiltReceiptModel } from "../samples-dev/prebuilt/prebuilt-receipt.js";

const credential = new DefaultAzureCredential();
const client = new DocumentAnalysisClient(
  "https://<resource name>.cognitiveservices.azure.com",
  credential,
);

const poller = await client.beginAnalyzeDocumentFromUrl(
  PrebuiltReceiptModel,
  // The Document Intelligence service will access the following URL to a receipt image and extract data from it
  "https://raw.githubusercontent.com/Azure/azure-sdk-for-js/main/sdk/formrecognizer/ai-form-recognizer/assets/receipt/contoso-receipt.png",
);

const {
  documents: [document],
} = await poller.pollUntilDone();

// Use of PrebuiltModels.Receipt above (rather than the raw model ID), as it adds strong typing of the model's output
if (document) {
  const { merchantName, items, total } = document.fields;

  console.log("=== Receipt Information ===");
  console.log("Type:", document.docType);
  console.log("Merchant:", merchantName && merchantName.value);

  console.log("Items:");
  for (const item of (items && items.values) || []) {
    const { description, totalPrice } = item.properties;

    console.log("- Description:", description && description.value);
    console.log("  Total Price:", totalPrice && totalPrice.value);
  }

  console.log("Total:", total && total.value);
} else {
  throw new Error("Expected at least one receipt in the result.");
}

function beginAnalyzeDocumentFromUrl<Result>(model: DocumentModel<Result>, documentUrl: string, options?: AnalyzeDocumentOptions<Result>): Promise<AnalysisPoller<Result>>

參數

model: DocumentModel<Result>

DocumentModel，代表要用於分析和預期輸出類型的模型

documentUrl: string

可從公用因特網存取之輸入檔案的網址（字串）

options: AnalyzeDocumentOptions<Result>

分析作業和輪詢器選擇性設定

傳回

Promise<AnalysisPoller<Result>>

長時間執行的作業（投票器）最終會產生 AnalyzeResult

beginClassifyDocument(string, FormRecognizerRequestBody, ClassifyDocumentOptions)

使用識別碼所指定的自定義分類器來分類檔。

這個方法會產生長時間執行的作業（poller），最終會產生 AnalyzeResult。這與 beginAnalyzeDocument 和 beginAnalyzeDocumentFromUrl相同，但結果只會包含其字段的小子集。只會填入 [documents] 字段和 [pages] 字段，而且只會傳回最少的頁面資訊。 [documents] 字段將包含所有已識別檔及其分類為 docType 的相關信息。

例

此方法支援可串流要求主體（FormRecognizerRequestBody），例如Node.JS ReadableStream对象、瀏覽器 Blob和 ArrayBuffers。本文的內容將會上傳至服務進行分析。

import { DefaultAzureCredential } from "@azure/identity";
import { DocumentAnalysisClient } from "@azure/ai-form-recognizer";
import { createReadStream } from "node:fs";

const credential = new DefaultAzureCredential();
const client = new DocumentAnalysisClient(
  "https://<resource name>.cognitiveservices.azure.com",
  credential,
);

const path = "<path to a document>";
const readStream = createReadStream(path);

const poller = await client.beginClassifyDocument("<classifier id>", readStream);

const result = await poller.pollUntilDone();

if (result?.documents?.length === 0) {
  throw new Error("Failed to extract any documents.");
}

for (const document of result.documents) {
  console.log(
    `Extracted a document with type '${document.docType}' on page ${document.boundingRegions?.[0].pageNumber} (confidence: ${document.confidence})`,
  );
}

function beginClassifyDocument(classifierId: string, document: FormRecognizerRequestBody, options?: ClassifyDocumentOptions): Promise<AnalysisPoller<AnalyzeResult<AnalyzedDocument>>>

參數

classifierId: string

要用於分析之自定義分類器的標識碼

document: FormRecognizerRequestBody

要分類的檔

options: ClassifyDocumentOptions

分類作業的選項

傳回

Promise<AnalysisPoller<AnalyzeResult<AnalyzedDocument>>>

長時間執行的作業（投票器）最終會產生 AnalyzeResult

beginClassifyDocumentFromUrl(string, string, ClassifyDocumentOptions)

使用標識碼所提供的自定義分類器，從 URL 分類檔。

例

import { DefaultAzureCredential } from "@azure/identity";
import { DocumentAnalysisClient } from "@azure/ai-form-recognizer";

const credential = new DefaultAzureCredential();
const client = new DocumentAnalysisClient(
  "https://<resource name>.cognitiveservices.azure.com",
  credential,
);

const documentUrl =
  "https://raw.githubusercontent.com/Azure/azure-sdk-for-js/main/sdk/formrecognizer/ai-form-recognizer/assets/invoice/Invoice_1.pdf";

const poller = await client.beginClassifyDocumentFromUrl("<classifier id>", documentUrl);

const result = await poller.pollUntilDone();

if (result?.documents?.length === 0) {
  throw new Error("Failed to extract any documents.");
}

for (const document of result.documents) {
  console.log(
    `Extracted a document with type '${document.docType}' on page ${document.boundingRegions?.[0].pageNumber} (confidence: ${document.confidence})`,
  );
}

function beginClassifyDocumentFromUrl(classifierId: string, documentUrl: string, options?: ClassifyDocumentOptions): Promise<AnalysisPoller<AnalyzeResult<AnalyzedDocument>>>

參數

classifierId: string

要用於分析之自定義分類器的標識碼

documentUrl: string

要分類的檔URL

options: ClassifyDocumentOptions

傳回

Promise<AnalysisPoller<AnalyzeResult<AnalyzedDocument>>>

共用方式為

DocumentAnalysisClient class

例子：

Azure Active Directory

API 金鑰 （訂用帳戶金鑰）

建構函式

例：

例：

方法

例子

例子

例子

例子

例

例

建構函式詳細資料

DocumentAnalysisClient(string, KeyCredential, DocumentAnalysisClientOptions)

例：

參數

DocumentAnalysisClient(string, TokenCredential, DocumentAnalysisClientOptions)

例：

參數

方法詳細資料

beginAnalyzeDocument(string, FormRecognizerRequestBody, AnalyzeDocumentOptions<AnalyzeResult<AnalyzedDocument>>)

例子

參數

傳回

beginAnalyzeDocument<Result>(DocumentModel<Result>, FormRecognizerRequestBody, AnalyzeDocumentOptions<Result>)

例子

參數

傳回

beginAnalyzeDocumentFromUrl(string, string, AnalyzeDocumentOptions<AnalyzeResult<AnalyzedDocument>>)

例子

參數

傳回

beginAnalyzeDocumentFromUrl<Result>(DocumentModel<Result>, string, AnalyzeDocumentOptions<Result>)

例子

參數

傳回

beginClassifyDocument(string, FormRecognizerRequestBody, ClassifyDocumentOptions)

例

參數

傳回

beginClassifyDocumentFromUrl(string, string, ClassifyDocumentOptions)

例

參數

傳回

API 金鑰（訂用帳戶金鑰）