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DocumentAnalysisClient class

包:

@azure/ai-form-recognizer

用于与表单识别器服务分析功能的交互的客户端。

例子：

表单识别器服务和客户端支持两种身份验证方式：

Azure Active Directory

JavaScript

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

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

const client = new DocumentAnalysisClient(endpoint, credential);

API 密钥（订阅密钥）

JavaScript

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

const endpoint = "https://<resource name>.cognitiveservices.azure.com";
const credential = new AzureKeyCredential("<api key>");

const client = new DocumentAnalysisClient(endpoint, credential);

构造函数

DocumentAnalysisClient(string, KeyCredential, DocumentAnalysisClientOptions)

从资源终结点和静态 API 密钥（KeyCredential）创建 DocumentAnalysisClient 实例 例： JavaScript import { DocumentAnalysisClient, AzureKeyCredential } from "@azure/ai-form-recognizer"; const endpoint = "https://<resource name>.cognitiveservices.azure.com"; const credential = new AzureKeyCredential("<api key>"); const client = new DocumentAnalysisClient(endpoint, credential);

DocumentAnalysisClient(string, TokenCredential, DocumentAnalysisClientOptions)

从资源终结点和 Azure 标识 TokenCredential创建 DocumentAnalysisClient 实例。 有关使用 Azure Active Directory 进行身份验证的详细信息，请参阅 @azure/identity 包。 例： JavaScript import { DocumentAnalysisClient } from "@azure/ai-form-recognizer"; import { DefaultAzureCredential } from "@azure/identity"; const endpoint = "https://<resource name>.cognitiveservices.azure.com"; const credential = new DefaultAzureCredential(); const client = new DocumentAnalysisClient(endpoint, credential);

方法

beginAnalyzeDocument(string, FormRecognizerRequestBody, AnalyzeDocumentOptions<AnalyzeResult<AnalyzedDocument>>)	使用模型的唯一 ID 从输入中提取数据。 此作支持自定义模型和预生成模型。 例如，若要使用预生成的发票模型，请提供模型 ID“prebuilt-invoice”，或使用更简单的预生成布局模型，提供模型 ID“prebuilt-layout”。 AnalyzeResult 中生成的字段取决于用于分析的模型，并且任何提取的文档字段中的值取决于模型中的文档类型（如果有）及其相应的字段架构。 例子 此方法支持可流式传输的请求正文（FormRecognizerRequestBody），例如Node.JS ReadableStream对象、浏览器 Blob和 ArrayBuffer。 正文的内容将上传到服务进行分析。 JavaScript import * as fs from "fs"; const file = fs.createReadStream("path/to/receipt.pdf"); // The model that is passed to the following function call determines the type of the eventual result. In the // example, we will use the prebuilt receipt model, but you could use a custom model ID/name instead. const poller = await client.beginAnalyzeDocument("prebuilt-receipt", file); // The result is a long-running operation (poller), which must itself be polled until the operation completes const { pages, // pages extracted from the document, which contain lines and words tables, // extracted tables, organized into cells that contain their contents styles, // text styles (ex. handwriting) that were observed in the document keyValuePairs, // extracted pairs of elements (directed associations from one element in the input to another) entities, // extracted entities in the input's content, which are categorized (ex. "Location" or "Organization") documents // extracted documents (instances of one of the model's document types and its field schema) } = await poller.pollUntilDone(); // Extract the fields of the first document. These fields constitute a receipt, because we used the receipt model const [{ fields: receipt }] = documents; // The fields correspond to the model's document types and their field schemas. Refer to the Form Recognizer // documentation for information about the document types and field schemas within a model, or use the `getModel` // operation to view this information programmatically. console.log("The type of this receipt is:", receipt?.["ReceiptType"]?.value);
beginAnalyzeDocument<Result>(DocumentModel<Result>, FormRecognizerRequestBody, AnalyzeDocumentOptions<Result>)	使用具有已知强类型文档架构的模型（DocumentModel）从输入中提取数据。 AnalyzeResult 中生成的字段取决于用于分析的模型。 在 TypeScript 中，此方法重载的结果类型从输入 DocumentModel的类型推断。 例子 此方法支持可流式传输的请求正文（FormRecognizerRequestBody），例如Node.JS ReadableStream对象、浏览器 Blob和 ArrayBuffer。 正文的内容将上传到服务进行分析。 如果提供的输入是字符串，它将被视为要分析的文档位置的 URL。 有关详细信息，请参阅 beginAnalyzeDocumentFromUrl 方法。 使用 URL 时，首选使用此方法，并且仅在此方法中提供 URL 支持以实现向后兼容性。 TypeScript import * as fs from "fs"; // See the `prebuilt` folder in the SDK samples (http://aka.ms/azsdk/formrecognizer/js/samples) for examples of // DocumentModels for known prebuilts. import { PrebuiltReceiptModel } from "./prebuilt-receipt.ts"; const file = fs.createReadStream("path/to/receipt.pdf"); // The model that is passed to the following function call determines the type of the eventual result. In the // example, we will use the prebuilt receipt model. const poller = await client.beginAnalyzeDocument(PrebuiltReceiptModel, file); // The result is a long-running operation (poller), which must itself be polled until the operation completes const { pages, // pages extracted from the document, which contain lines and words tables, // extracted tables, organized into cells that contain their contents styles, // text styles (ex. handwriting) that were observed in the document keyValuePairs, // extracted pairs of elements (directed associations from one element in the input to another) documents // extracted documents (instances of one of the model's document types and its field schema) } = await poller.pollUntilDone(); // Extract the fields of the first document. These fields constitute a receipt, because we used the receipt model const [{ fields: receipt }] = documents; // Since we used the strongly-typed PrebuiltReceiptModel object instead of the "prebuilt-receipt" model ID // string, the fields of the receipt are strongly-typed and have camelCase names (as opposed to PascalCase). console.log("The type of this receipt is:", receipt.receiptType?.value);
beginAnalyzeDocumentFromUrl(string, string, AnalyzeDocumentOptions<AnalyzeResult<AnalyzedDocument>>)	使用模型的唯一 ID 从输入中提取数据。 此作支持自定义模型和预生成模型。 例如，若要使用预生成的发票模型，请提供模型 ID“prebuilt-invoice”，或使用更简单的预生成布局模型，提供模型 ID“prebuilt-layout”。 AnalyzeResult 中生成的字段取决于用于分析的模型，并且任何提取的文档字段中的值取决于模型中的文档类型（如果有）及其相应的字段架构。 例子 此方法支持从给定 URL 的文件中提取数据。 表单识别器服务将尝试使用提交的 URL 下载文件，因此必须可从公共 Internet 访问该 URL。 例如，SAS 令牌可用于授予对 Azure 存储中 Blob 的读取访问权限，服务将使用 SAS 编码的 URL 请求文件。 JavaScript // the URL must be publicly accessible const url = "<receipt document url>"; // The model that is passed to the following function call determines the type of the eventual result. In the // example, we will use the prebuilt receipt model, but you could use a custom model ID/name instead. const poller = await client.beginAnalyzeDocument("prebuilt-receipt", url); // The result is a long-running operation (poller), which must itself be polled until the operation completes const { pages, // pages extracted from the document, which contain lines and words tables, // extracted tables, organized into cells that contain their contents styles, // text styles (ex. handwriting) that were observed in the document keyValuePairs, // extracted pairs of elements (directed associations from one element in the input to another) documents // extracted documents (instances of one of the model's document types and its field schema) } = await poller.pollUntilDone(); // Extract the fields of the first document. These fields constitute a receipt, because we used the receipt model const [{ fields: receipt }] = documents; // The fields correspond to the model's document types and their field schemas. Refer to the Form Recognizer // documentation for information about the document types and field schemas within a model, or use the `getModel` // operation to view this information programmatically. console.log("The type of this receipt is:", receipt?.["ReceiptType"]?.value);
beginAnalyzeDocumentFromUrl<Result>(DocumentModel<Result>, string, AnalyzeDocumentOptions<Result>)	使用具有已知强类型文档架构的模型（DocumentModel）从输入中提取数据。 AnalyzeResult 中生成的字段取决于用于分析的模型。 在 TypeScript 中，此方法重载的结果类型从输入 DocumentModel的类型推断。 例子 此方法支持从给定 URL 的文件中提取数据。 表单识别器服务将尝试使用提交的 URL 下载文件，因此必须可从公共 Internet 访问该 URL。 例如，SAS 令牌可用于授予对 Azure 存储中 Blob 的读取访问权限，服务将使用 SAS 编码的 URL 请求文件。 TypeScript // See the `prebuilt` folder in the SDK samples (http://aka.ms/azsdk/formrecognizer/js/samples) for examples of // DocumentModels for known prebuilts. import { PrebuiltReceiptModel } from "./prebuilt-receipt.ts"; // the URL must be publicly accessible const url = "<receipt document url>"; // The model that is passed to the following function call determines the type of the eventual result. In the // example, we will use the prebuilt receipt model. const poller = await client.beginAnalyzeDocument(PrebuiltReceiptModel, url); // The result is a long-running operation (poller), which must itself be polled until the operation completes const { pages, // pages extracted from the document, which contain lines and words tables, // extracted tables, organized into cells that contain their contents styles, // text styles (ex. handwriting) that were observed in the document keyValuePairs, // extracted pairs of elements (directed associations from one element in the input to another) documents // extracted documents (instances of one of the model's document types and its field schema) } = await poller.pollUntilDone(); // Extract the fields of the first document. These fields constitute a receipt, because we used the receipt model const [{ fields: receipt }] = documents; // Since we used the strongly-typed PrebuiltReceiptModel object instead of the "prebuilt-receipt" model ID // string, the fields of the receipt are strongly-typed and have camelCase names (as opposed to PascalCase). console.log("The type of this receipt is:", receipt.receiptType?.value);
beginClassifyDocument(string, FormRecognizerRequestBody, ClassifyDocumentOptions)	使用其 ID 提供的自定义分类器对文档进行分类。 此方法生成一个长时间运行的作（轮询器），最终将生成 AnalyzeResult。 此类型与 beginAnalyzeDocument 和 beginAnalyzeDocumentFromUrl相同，但结果将仅包含其字段的一小部分。 仅填充 documents 字段和 pages 字段，只返回最少的页面信息。 documents 字段将包含有关所有已标识文档及其分类为 docType 的信息。 例 此方法支持可流式传输的请求正文（FormRecognizerRequestBody），例如Node.JS ReadableStream对象、浏览器 Blob和 ArrayBuffer。 正文的内容将上传到服务进行分析。 TypeScript import * as fs from "fs"; const file = fs.createReadStream("path/to/file.pdf"); const poller = await client.beginClassifyDocument("<classifier ID>", file); // The result is a long-running operation (poller), which must itself be polled until the operation completes const { pages, // pages extracted from the document, which contain only basic information for classifiers documents // extracted documents and their types } = await poller.pollUntilDone(); // We'll print the documents and their types for (const { docType } of documents) { console.log("The type of this document is:", docType); }
beginClassifyDocumentFromUrl(string, string, ClassifyDocumentOptions)	使用 ID 提供的自定义分类器从 URL 对文档进行分类。 此方法生成一个长时间运行的作（轮询器），最终将生成 AnalyzeResult。 此类型与 beginAnalyzeDocument 和 beginAnalyzeDocumentFromUrl相同，但结果将仅包含其字段的一小部分。 仅填充 documents 字段和 pages 字段，只返回最少的页面信息。 documents 字段将包含有关所有已标识文档及其分类为 docType 的信息。 例 此方法支持从给定 URL 的文件中提取数据。 表单识别器服务将尝试使用提交的 URL 下载文件，因此必须可从公共 Internet 访问该 URL。 例如，SAS 令牌可用于授予对 Azure 存储中 Blob 的读取访问权限，服务将使用 SAS 编码的 URL 请求文件。 TypeScript // the URL must be publicly accessible const url = "<file url>"; const poller = await client.beginClassifyDocument("<classifier ID>", url); // The result is a long-running operation (poller), which must itself be polled until the operation completes const { pages, // pages extracted from the document, which contain only basic information for classifiers documents // extracted documents and their types } = await poller.pollUntilDone(); // We'll print the documents and their types for (const { docType } of documents) { console.log("The type of this document is:", docType); }

构造函数详细信息

DocumentAnalysisClient(string, KeyCredential, DocumentAnalysisClientOptions)

从资源终结点和静态 API 密钥（KeyCredential）创建 DocumentAnalysisClient 实例

例：

JavaScript

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

const endpoint = "https://<resource name>.cognitiveservices.azure.com";
const credential = new AzureKeyCredential("<api key>");

const client = new DocumentAnalysisClient(endpoint, credential);

TypeScript

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

参数

endpoint

string

Azure 认知服务实例的终结点 URL

credential

KeyCredential

包含认知服务实例订阅密钥的 KeyCredential

options

DocumentAnalysisClientOptions

用于配置客户端中所有方法的可选设置

DocumentAnalysisClient(string, TokenCredential, DocumentAnalysisClientOptions)

从资源终结点和 Azure 标识 TokenCredential创建 DocumentAnalysisClient 实例。

有关使用 Azure Active Directory 进行身份验证的详细信息，请参阅 @azure/identity 包。

例：

JavaScript

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

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

const client = new DocumentAnalysisClient(endpoint, credential);

TypeScript

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

参数

endpoint

string

Azure 认知服务实例的终结点 URL

credential

TokenCredential

@azure/identity 包中的 TokenCredential 实例

options

DocumentAnalysisClientOptions

用于配置客户端中所有方法的可选设置

方法详细信息

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

使用模型的唯一 ID 从输入中提取数据。

此作支持自定义模型和预生成模型。 例如，若要使用预生成的发票模型，请提供模型 ID“prebuilt-invoice”，或使用更简单的预生成布局模型，提供模型 ID“prebuilt-layout”。

AnalyzeResult 中生成的字段取决于用于分析的模型，并且任何提取的文档字段中的值取决于模型中的文档类型（如果有）及其相应的字段架构。

例子

此方法支持可流式传输的请求正文（FormRecognizerRequestBody），例如Node.JS ReadableStream对象、浏览器 Blob和 ArrayBuffer。 正文的内容将上传到服务进行分析。

JavaScript

import * as fs from "fs";

const file = fs.createReadStream("path/to/receipt.pdf");

// The model that is passed to the following function call determines the type of the eventual result. In the
// example, we will use the prebuilt receipt model, but you could use a custom model ID/name instead.
const poller = await client.beginAnalyzeDocument("prebuilt-receipt", file);

// The result is a long-running operation (poller), which must itself be polled until the operation completes
const {
  pages, // pages extracted from the document, which contain lines and words
  tables, // extracted tables, organized into cells that contain their contents
  styles, // text styles (ex. handwriting) that were observed in the document
  keyValuePairs, // extracted pairs of elements  (directed associations from one element in the input to another)
  entities, // extracted entities in the input's content, which are categorized (ex. "Location" or "Organization")
  documents // extracted documents (instances of one of the model's document types and its field schema)
} = await poller.pollUntilDone();

// Extract the fields of the first document. These fields constitute a receipt, because we used the receipt model
const [{ fields: receipt }] = documents;

// The fields correspond to the model's document types and their field schemas. Refer to the Form Recognizer
// documentation for information about the document types and field schemas within a model, or use the `getModel`
// operation to view this information programmatically.
console.log("The type of this receipt is:", receipt?.["ReceiptType"]?.value);

TypeScript

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

参数

modelId

string

此客户端资源中模型的唯一 ID （名称）

document

FormRecognizerRequestBody

将随请求一起上传的 FormRecognizerRequestBody

options

AnalyzeDocumentOptions<AnalyzeResult<AnalyzedDocument>>

分析作和轮询器的可选设置

返回

Promise<AnalysisPoller<AnalyzeResult<AnalyzedDocument>>>

一个长时间运行的作（轮询器），最终将产生 AnalyzeResult

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

使用具有已知强类型文档架构的模型（DocumentModel）从输入中提取数据。

AnalyzeResult 中生成的字段取决于用于分析的模型。 在 TypeScript 中，此方法重载的结果类型从输入 DocumentModel的类型推断。

例子

此方法支持可流式传输的请求正文（FormRecognizerRequestBody），例如Node.JS ReadableStream对象、浏览器 Blob和 ArrayBuffer。 正文的内容将上传到服务进行分析。

如果提供的输入是字符串，它将被视为要分析的文档位置的 URL。 有关详细信息，请参阅 beginAnalyzeDocumentFromUrl 方法。 使用 URL 时，首选使用此方法，并且仅在此方法中提供 URL 支持以实现向后兼容性。

TypeScript

import * as fs from "fs";

// See the `prebuilt` folder in the SDK samples (http://aka.ms/azsdk/formrecognizer/js/samples) for examples of
// DocumentModels for known prebuilts.
import { PrebuiltReceiptModel } from "./prebuilt-receipt.ts";

const file = fs.createReadStream("path/to/receipt.pdf");

// The model that is passed to the following function call determines the type of the eventual result. In the
// example, we will use the prebuilt receipt model.
const poller = await client.beginAnalyzeDocument(PrebuiltReceiptModel, file);

// The result is a long-running operation (poller), which must itself be polled until the operation completes
const {
  pages, // pages extracted from the document, which contain lines and words
  tables, // extracted tables, organized into cells that contain their contents
  styles, // text styles (ex. handwriting) that were observed in the document
  keyValuePairs, // extracted pairs of elements  (directed associations from one element in the input to another)

  documents // extracted documents (instances of one of the model's document types and its field schema)
} = await poller.pollUntilDone();

// Extract the fields of the first document. These fields constitute a receipt, because we used the receipt model
const [{ fields: receipt }] = documents;

// Since we used the strongly-typed PrebuiltReceiptModel object instead of the "prebuilt-receipt" model ID
// string, the fields of the receipt are strongly-typed and have camelCase names (as opposed to PascalCase).
console.log("The type of this receipt is:", receipt.receiptType?.value);

TypeScript

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

参数

model

DocumentModel<Result>

DocumentModel 表示要用于分析和预期输出类型的模型

document

FormRecognizerRequestBody

将随请求一起上传的 FormRecognizerRequestBody

options

AnalyzeDocumentOptions<Result>

分析作和轮询器的可选设置

返回

Promise<AnalysisPoller<Result>>

一个长时间运行的作（轮询器），最终将生成一个具有与输入模型关联的结果类型的文档的 AnalyzeResult

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

使用模型的唯一 ID 从输入中提取数据。

此作支持自定义模型和预生成模型。 例如，若要使用预生成的发票模型，请提供模型 ID“prebuilt-invoice”，或使用更简单的预生成布局模型，提供模型 ID“prebuilt-layout”。

AnalyzeResult 中生成的字段取决于用于分析的模型，并且任何提取的文档字段中的值取决于模型中的文档类型（如果有）及其相应的字段架构。

例子

此方法支持从给定 URL 的文件中提取数据。 表单识别器服务将尝试使用提交的 URL 下载文件，因此必须可从公共 Internet 访问该 URL。 例如，SAS 令牌可用于授予对 Azure 存储中 Blob 的读取访问权限，服务将使用 SAS 编码的 URL 请求文件。

JavaScript

// the URL must be publicly accessible
const url = "<receipt document url>";

// The model that is passed to the following function call determines the type of the eventual result. In the
// example, we will use the prebuilt receipt model, but you could use a custom model ID/name instead.
const poller = await client.beginAnalyzeDocument("prebuilt-receipt", url);

// The result is a long-running operation (poller), which must itself be polled until the operation completes
const {
  pages, // pages extracted from the document, which contain lines and words
  tables, // extracted tables, organized into cells that contain their contents
  styles, // text styles (ex. handwriting) that were observed in the document
  keyValuePairs, // extracted pairs of elements  (directed associations from one element in the input to another)

  documents // extracted documents (instances of one of the model's document types and its field schema)
} = await poller.pollUntilDone();

// Extract the fields of the first document. These fields constitute a receipt, because we used the receipt model
const [{ fields: receipt }] = documents;

// The fields correspond to the model's document types and their field schemas. Refer to the Form Recognizer
// documentation for information about the document types and field schemas within a model, or use the `getModel`
// operation to view this information programmatically.
console.log("The type of this receipt is:", receipt?.["ReceiptType"]?.value);

TypeScript

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

参数

modelId

string

此客户端资源中模型的唯一 ID （名称）

documentUrl

string

可从公共 Internet 访问的输入文档的 URL（字符串）

options

AnalyzeDocumentOptions<AnalyzeResult<AnalyzedDocument>>

分析作和轮询器的可选设置

返回

Promise<AnalysisPoller<AnalyzeResult<AnalyzedDocument>>>

一个长时间运行的作（轮询器），最终将产生 AnalyzeResult

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

使用具有已知强类型文档架构的模型（DocumentModel）从输入中提取数据。

AnalyzeResult 中生成的字段取决于用于分析的模型。 在 TypeScript 中，此方法重载的结果类型从输入 DocumentModel的类型推断。

例子

此方法支持从给定 URL 的文件中提取数据。 表单识别器服务将尝试使用提交的 URL 下载文件，因此必须可从公共 Internet 访问该 URL。 例如，SAS 令牌可用于授予对 Azure 存储中 Blob 的读取访问权限，服务将使用 SAS 编码的 URL 请求文件。

TypeScript

// See the `prebuilt` folder in the SDK samples (http://aka.ms/azsdk/formrecognizer/js/samples) for examples of
// DocumentModels for known prebuilts.
import { PrebuiltReceiptModel } from "./prebuilt-receipt.ts";

// the URL must be publicly accessible
const url = "<receipt document url>";

// The model that is passed to the following function call determines the type of the eventual result. In the
// example, we will use the prebuilt receipt model.
const poller = await client.beginAnalyzeDocument(PrebuiltReceiptModel, url);

// The result is a long-running operation (poller), which must itself be polled until the operation completes
const {
  pages, // pages extracted from the document, which contain lines and words
  tables, // extracted tables, organized into cells that contain their contents
  styles, // text styles (ex. handwriting) that were observed in the document
  keyValuePairs, // extracted pairs of elements  (directed associations from one element in the input to another)

  documents // extracted documents (instances of one of the model's document types and its field schema)
} = await poller.pollUntilDone();

// Extract the fields of the first document. These fields constitute a receipt, because we used the receipt model
const [{ fields: receipt }] = documents;

// Since we used the strongly-typed PrebuiltReceiptModel object instead of the "prebuilt-receipt" model ID
// string, the fields of the receipt are strongly-typed and have camelCase names (as opposed to PascalCase).
console.log("The type of this receipt is:", receipt.receiptType?.value);

TypeScript

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

参数

model

DocumentModel<Result>

DocumentModel 表示要用于分析和预期输出类型的模型

documentUrl

string

可从公共 Internet 访问的输入文档的 URL（字符串）

options

AnalyzeDocumentOptions<Result>

分析作和轮询器的可选设置

返回

Promise<AnalysisPoller<Result>>

一个长时间运行的作（轮询器），最终将产生 AnalyzeResult

beginClassifyDocument(string, FormRecognizerRequestBody, ClassifyDocumentOptions)

使用其 ID 提供的自定义分类器对文档进行分类。

此方法生成一个长时间运行的作（轮询器），最终将生成 AnalyzeResult。 此类型与 beginAnalyzeDocument 和 beginAnalyzeDocumentFromUrl相同，但结果将仅包含其字段的一小部分。 仅填充 documents 字段和 pages 字段，只返回最少的页面信息。 documents 字段将包含有关所有已标识文档及其分类为 docType 的信息。

例

此方法支持可流式传输的请求正文（FormRecognizerRequestBody），例如Node.JS ReadableStream对象、浏览器 Blob和 ArrayBuffer。 正文的内容将上传到服务进行分析。

TypeScript

import * as fs from "fs";

const file = fs.createReadStream("path/to/file.pdf");

const poller = await client.beginClassifyDocument("<classifier ID>", file);

// The result is a long-running operation (poller), which must itself be polled until the operation completes
const {
  pages, // pages extracted from the document, which contain only basic information for classifiers
  documents // extracted documents and their types
} = await poller.pollUntilDone();

// We'll print the documents and their types
for (const { docType } of documents) {
  console.log("The type of this document is:", docType);
}

TypeScript

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

参数

classifierId

string

要用于分析的自定义分类器的 ID

document

FormRecognizerRequestBody

要分类的文档

options

ClassifyDocumentOptions

分类作的选项

返回

Promise<AnalysisPoller<AnalyzeResult<AnalyzedDocument>>>

一个长时间运行的作（轮询器），最终将产生 AnalyzeResult

beginClassifyDocumentFromUrl(string, string, ClassifyDocumentOptions)

使用 ID 提供的自定义分类器从 URL 对文档进行分类。

此方法生成一个长时间运行的作（轮询器），最终将生成 AnalyzeResult。 此类型与 beginAnalyzeDocument 和 beginAnalyzeDocumentFromUrl相同，但结果将仅包含其字段的一小部分。 仅填充 documents 字段和 pages 字段，只返回最少的页面信息。 documents 字段将包含有关所有已标识文档及其分类为 docType 的信息。

例

此方法支持从给定 URL 的文件中提取数据。 表单识别器服务将尝试使用提交的 URL 下载文件，因此必须可从公共 Internet 访问该 URL。 例如，SAS 令牌可用于授予对 Azure 存储中 Blob 的读取访问权限，服务将使用 SAS 编码的 URL 请求文件。

TypeScript

// the URL must be publicly accessible
const url = "<file url>";

const poller = await client.beginClassifyDocument("<classifier ID>", url);

// The result is a long-running operation (poller), which must itself be polled until the operation completes
const {
  pages, // pages extracted from the document, which contain only basic information for classifiers
  documents // extracted documents and their types
} = await poller.pollUntilDone();

// We'll print the documents and their types
for (const { docType } of documents) {
  console.log("The type of this document is:", docType);
}

TypeScript

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

参数

classifierId

string

要用于分析的自定义分类器的 ID

documentUrl

string

要分类的文档的 URL

options

ClassifyDocumentOptions

返回

Promise<AnalysisPoller<AnalyzeResult<AnalyzedDocument>>>