TextAnalysisClient class

  • Reference
Package:
@azure/ai-text-analytics

A client for interacting with the text analysis features in Azure Cognitive Language Service.

The client needs the endpoint of a Language resource and an authentication method such as an API key or AAD. The API key and endpoint can be found in the Language resource page in the Azure portal. They will be located in the resource's Keys and Endpoint page, under Resource Management.

Examples for authentication:

API Key

import { TextAnalysisClient, AzureKeyCredential } from "@azure/ai-text-analytics";

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

const client = new TextAnalysisClient(endpoint, credential);

Azure Active Directory

See the @azure/identity package for more information about authenticating with Azure Active Directory.

import { TextAnalysisClient } from "@azure/ai-text-analytics";
import { DefaultAzureCredential } from "@azure/identity";

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

const client = new TextAnalysisClient(endpoint, credential);

Constructors

TextAnalysisClient(string, KeyCredential, TextAnalysisClientOptions)

Creates an instance of TextAnalysisClient with the endpoint of a Language resource and an authentication method such as an API key or AAD.

The API key and endpoint can be found in the Language resource page in the Azure portal. They will be located in the resource's Keys and Endpoint page, under Resource Management.

Example

import { TextAnalysisClient, AzureKeyCredential } from "@azure/ai-text-analytics";

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

const client = new TextAnalysisClient(endpoint, credential);
TextAnalysisClient(string, TokenCredential, TextAnalysisClientOptions)

Creates an instance of TextAnalysisClient with the endpoint of a Language resource and an authentication method such as an API key or AAD.

The API key and endpoint can be found in the Language resource page in the Azure portal. They will be located in the resource's Keys and Endpoint page, under Resource Management.

Example

See the @azure/identity package for more information about authenticating with Azure Active Directory.

import { TextAnalysisClient } from "@azure/ai-text-analytics";
import { DefaultAzureCredential } from "@azure/identity";

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

const client = new TextAnalysisClient(endpoint, credential);

Methods

analyze<ActionName>(ActionName, LanguageDetectionInput[], AnalyzeActionParameters<ActionName> & TextAnalysisOperationOptions)

Runs a predictive model to determine the language that the passed-in input strings are written in, and returns, for each one, the detected language as well as a score indicating the model's confidence that the inferred language is correct. Scores close to 1 indicate high certainty in the result. 120 languages are supported.

See https://docs.microsoft.com//azure/cognitive-services/language-service/concepts/data-limits for data limits.

Examples

Language detection

const documents = [<input strings>];
const countryHint = "us";
const results = await client.analyze("LanguageDetection", documents, countryHint);

for (let i = 0; i < results.length; i++) {
  const result = results[i];
  if (result.error) {
    // a document has an error instead of results
  } else {
    const { name, confidenceScore, iso6391Name } = result.primaryLanguage;
  }
}

See https://docs.microsoft.com//azure/cognitive-services/language-service/language-detection/overview for more information on language detection.
analyze<ActionName>(ActionName, string[], string, AnalyzeActionParameters<ActionName> & TextAnalysisOperationOptions)

Runs a predictive model to determine the language that the passed-in input strings are written in, and returns, for each one, the detected language as well as a score indicating the model's confidence that the inferred language is correct. Scores close to 1 indicate high certainty in the result. 120 languages are supported.

See https://docs.microsoft.com//azure/cognitive-services/language-service/concepts/data-limits for data limits.

Examples

Language detection

const documents = [<input strings>];
const countryHint = "us";
const results = await client.analyze("LanguageDetection", documents, countryHint);

for (const result of results) {
  if (result.error) {
    // a document has an error instead of results
  } else {
    const { name, confidenceScore, iso6391Name } = result.primaryLanguage;
  }
}

See https://docs.microsoft.com//azure/cognitive-services/language-service/language-detection/overview for more information on language detection.
analyze<ActionName>(ActionName, string[], string, AnalyzeActionParameters<ActionName> & TextAnalysisOperationOptions)

Runs a predictive model to perform the action of choice on the input strings. See $AnalyzeActionName for a list of supported actions.

The layout of each item in the results array depends on the action chosen. For example, each PIIEntityRecognition document result consists of both entities and redactedText where the former is a list of all Pii entities in the text and the latter is the original text after all such Pii entities have been redacted from it.

See https://docs.microsoft.com//azure/cognitive-services/language-service/concepts/data-limits for data limits.

Examples

Opinion mining

const documents = ["The food and service aren't the best"];
const results = await client.analyze("SentimentAnalysis", documents, {
  includeOpinionMining: true,
});

for (const result of results) {
  if (result.error) {
    // a document has an error instead of results
  } else {
    const { sentiment, confidenceScores, sentences } = result;
    for (const { sentiment, confidenceScores, opinions } of sentences) {
      for (const { target, assessments } of opinions) {
        const { text, sentiment, confidenceScores } = target;
        for (const { text, sentiment } of assessments) {
          // Do something
        }
      }
    }
  }
}

See https://docs.microsoft.com//azure/cognitive-services/language-service/sentiment-opinion-mining/overview for more information on opinion mining.

Personally identifiable information

const documents = [<input strings>];
const languageHint = "en";
const categoriesFilter = [KnownPiiCategory.USSocialSecurityNumber];
const domainFilter = KnownPiiDomain.Phi;
const results = await client.analyze("PiiEntityRecognition", documents, languageHint, {
  domainFilter, categoriesFilter
});

for (const result of results) {
  if (result.error) {
    // a document has an error instead of results
  } else {
    const { entities, redactedText } = result;
    for (const { text, category, confidenceScore, length, offset } of entities) {
      // Do something
    }
  }
}

See https://docs.microsoft.com//azure/cognitive-services/language-service/personally-identifiable-information/overview for more information on personally identifiable information.
analyze<ActionName>(ActionName, TextDocumentInput[], AnalyzeActionParameters<ActionName> & TextAnalysisOperationOptions)

Runs a predictive model to perform the action of choice on the input documents. See $AnalyzeActionName for a list of supported actions.

The layout of each item in the results array depends on the action chosen. For example, each PIIEntityRecognition document result consists of both entities and redactedText where the former is a list of all Pii entities in the text and the latter is the original text after all such Pii entities have been redacted from it.

See https://docs.microsoft.com//azure/cognitive-services/language-service/concepts/data-limits for data limits.

Examples

Opinion mining

const documents = [{
 id: "1",
 text: "The food and service aren't the best",
 language: "en"
}];
const results = await client.analyze("SentimentAnalysis", documents, {
  includeOpinionMining: true,
});

for (const result of results) {
  if (result.error) {
    // a document has an error instead of results
  } else {
    const { sentiment, confidenceScores, sentences } = result;
    for (const { sentiment, confidenceScores, opinions } of sentences) {
      for (const { target, assessments } of opinions) {
        const { text, sentiment, confidenceScores } = target;
        for (const { text, sentiment } of assessments) {
          // Do something
        }
      }
    }
  }
}

See https://docs.microsoft.com//azure/cognitive-services/language-service/sentiment-opinion-mining/overview for more information on opinion mining.

Personally identifiable information

const documents = [<input documents>];
const categoriesFilter = [KnownPiiCategory.USSocialSecurityNumber];
const domainFilter = KnownPiiDomain.Phi;
const results = await client.analyze("PiiEntityRecognition", documents, {
  domainFilter, categoriesFilter
});

for (const result of results) {
  if (result.error) {
    // a document has an error instead of results
  } else {
    const { entities, redactedText } = result;
    for (const { text, category, confidenceScore, length, offset } of entities) {
      // Do something
    }
  }
}

See https://docs.microsoft.com//azure/cognitive-services/language-service/personally-identifiable-information/overview for more information on personally identifiable information.
beginAnalyzeBatch(AnalyzeBatchAction[], string[], string, BeginAnalyzeBatchOptions)

Performs an array (batch) of actions on the input documents. Each action has a kind field that specifies the nature of the action. See $AnalyzeBatchActionNames for a list of supported actions. In addition to kind, actions could also have other parameters such as disableServiceLogs and modelVersion.

The results array contains the results for those input actions where each item also has a kind field that specifies the type of the results.

See https://docs.microsoft.com//azure/cognitive-services/language-service/concepts/data-limits for data limits.

Examples

Key phrase extraction and Pii entity recognition

const poller = await client.beginAnalyzeBatch(
 [{ kind: "KeyPhraseExtraction" }, { kind: "PiiEntityRecognition" }],
 documents
);
const actionResults = await poller.pollUntilDone();

for await (const actionResult of actionResults) {
 if (actionResult.error) {
   throw new Error(`Unexpected error`);
 }
 switch (actionResult.kind) {
   case "KeyPhraseExtraction": {
     for (const doc of actionResult.results) {
       // do something
     }
     break;
   }
   case "PiiEntityRecognition": {
     for (const doc of actionResult.results) {
       // do something
     }
     break;
   }
 }
}
beginAnalyzeBatch(AnalyzeBatchAction[], TextDocumentInput[], BeginAnalyzeBatchOptions)

Performs an array (batch) of actions on the input documents. Each action has a kind field that specifies the nature of the action. See $AnalyzeBatchActionNames for a list of supported actions. In addition to kind, actions could also have other parameters such as disableServiceLogs and modelVersion.

The results array contains the results for those input actions where each item also has a kind field that specifies the type of the results.

See https://docs.microsoft.com//azure/cognitive-services/language-service/concepts/data-limits for data limits.

Examples

Keyphrase extraction and Pii entity recognition

const poller = await client.beginAnalyzeBatch(
 [{ kind: "KeyPhraseExtraction" }, { kind: "PiiEntityRecognition" }],
 documents
);
const actionResults = await poller.pollUntilDone();

for await (const actionResult of actionResults) {
 if (actionResult.error) {
   throw new Error(`Unexpected error`);
 }
 switch (actionResult.kind) {
   case "KeyPhraseExtraction": {
     for (const doc of actionResult.results) {
       // do something
     }
     break;
   }
   case "PiiEntityRecognition": {
     for (const doc of actionResult.results) {
       // do something
     }
     break;
   }
 }
}
restoreAnalyzeBatchPoller(string, RestoreAnalyzeBatchPollerOptions)

Creates a poller from the serialized state of another poller. This can be useful when you want to create pollers on a different host or a poller needs to be constructed after the original one is not in scope.

Constructor Details

TextAnalysisClient(string, KeyCredential, TextAnalysisClientOptions)

Creates an instance of TextAnalysisClient with the endpoint of a Language resource and an authentication method such as an API key or AAD.

The API key and endpoint can be found in the Language resource page in the Azure portal. They will be located in the resource's Keys and Endpoint page, under Resource Management.

Example

import { TextAnalysisClient, AzureKeyCredential } from "@azure/ai-text-analytics";

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

const client = new TextAnalysisClient(endpoint, credential);

new TextAnalysisClient(endpointUrl: string, credential: KeyCredential, options?: TextAnalysisClientOptions)

Parameters

endpointUrl

string

The URL to the endpoint of a Cognitive Language Service resource

credential
KeyCredential

Key credential to be used to authenticate requests to the service.

options
TextAnalysisClientOptions

Used to configure the TextAnalytics client.

TextAnalysisClient(string, TokenCredential, TextAnalysisClientOptions)

Creates an instance of TextAnalysisClient with the endpoint of a Language resource and an authentication method such as an API key or AAD.

The API key and endpoint can be found in the Language resource page in the Azure portal. They will be located in the resource's Keys and Endpoint page, under Resource Management.

Example

See the @azure/identity package for more information about authenticating with Azure Active Directory.

import { TextAnalysisClient } from "@azure/ai-text-analytics";
import { DefaultAzureCredential } from "@azure/identity";

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

const client = new TextAnalysisClient(endpoint, credential);

new TextAnalysisClient(endpointUrl: string, credential: TokenCredential, options?: TextAnalysisClientOptions)

Parameters

endpointUrl

string

The URL to the endpoint of a Cognitive Language Service resource

credential
TokenCredential

Token credential to be used to authenticate requests to the service.

options
TextAnalysisClientOptions

Used to configure the TextAnalytics client.

Method Details

analyze<ActionName>(ActionName, LanguageDetectionInput[], AnalyzeActionParameters<ActionName> & TextAnalysisOperationOptions)

Runs a predictive model to determine the language that the passed-in input strings are written in, and returns, for each one, the detected language as well as a score indicating the model's confidence that the inferred language is correct. Scores close to 1 indicate high certainty in the result. 120 languages are supported.

See https://docs.microsoft.com//azure/cognitive-services/language-service/concepts/data-limits for data limits.

Examples

Language detection

const documents = [<input strings>];
const countryHint = "us";
const results = await client.analyze("LanguageDetection", documents, countryHint);

for (let i = 0; i < results.length; i++) {
  const result = results[i];
  if (result.error) {
    // a document has an error instead of results
  } else {
    const { name, confidenceScore, iso6391Name } = result.primaryLanguage;
  }
}

See https://docs.microsoft.com//azure/cognitive-services/language-service/language-detection/overview for more information on language detection.

function analyze<ActionName>(actionName: ActionName, documents: LanguageDetectionInput[], options?: AnalyzeActionParameters<ActionName> & TextAnalysisOperationOptions): Promise<AnalyzeResult<ActionName>>

Parameters

actionName

ActionName

the name of the action to be performed on the input documents, see $AnalyzeActionName

documents

LanguageDetectionInput[]

the input documents to be analyzed

options

AnalyzeActionParameters<ActionName> & TextAnalysisOperationOptions

optional action parameters and settings for the operation

Returns

Promise<AnalyzeResult<ActionName>>

an array of results where each element contains the primary language for the corresponding input document.

analyze<ActionName>(ActionName, string[], string, AnalyzeActionParameters<ActionName> & TextAnalysisOperationOptions)

Runs a predictive model to determine the language that the passed-in input strings are written in, and returns, for each one, the detected language as well as a score indicating the model's confidence that the inferred language is correct. Scores close to 1 indicate high certainty in the result. 120 languages are supported.

See https://docs.microsoft.com//azure/cognitive-services/language-service/concepts/data-limits for data limits.

Examples

Language detection

const documents = [<input strings>];
const countryHint = "us";
const results = await client.analyze("LanguageDetection", documents, countryHint);

for (const result of results) {
  if (result.error) {
    // a document has an error instead of results
  } else {
    const { name, confidenceScore, iso6391Name } = result.primaryLanguage;
  }
}

See https://docs.microsoft.com//azure/cognitive-services/language-service/language-detection/overview for more information on language detection.

function analyze<ActionName>(actionName: ActionName, documents: string[], countryHint?: string, options?: AnalyzeActionParameters<ActionName> & TextAnalysisOperationOptions): Promise<AnalyzeResult<ActionName>>

Parameters

actionName

ActionName

the name of the action to be performed on the input documents, see $AnalyzeActionName

documents

string[]

the input documents to be analyzed

countryHint

string

Indicates the country of origin for all of the input strings to assist the model in predicting the language they are written in. If unspecified, this value will be set to the default country hint in TextAnalysisClientOptions. If set to an empty string, or the string "none", the service will apply a model where the country is explicitly unset. The same country hint is applied to all strings in the input collection.

options

AnalyzeActionParameters<ActionName> & TextAnalysisOperationOptions

optional action parameters and settings for the operation

Returns

Promise<AnalyzeResult<ActionName>>

an array of results where each element contains the primary language for the corresponding input document.

analyze<ActionName>(ActionName, string[], string, AnalyzeActionParameters<ActionName> & TextAnalysisOperationOptions)

Runs a predictive model to perform the action of choice on the input strings. See $AnalyzeActionName for a list of supported actions.

The layout of each item in the results array depends on the action chosen. For example, each PIIEntityRecognition document result consists of both entities and redactedText where the former is a list of all Pii entities in the text and the latter is the original text after all such Pii entities have been redacted from it.

See https://docs.microsoft.com//azure/cognitive-services/language-service/concepts/data-limits for data limits.

Examples

Opinion mining

const documents = ["The food and service aren't the best"];
const results = await client.analyze("SentimentAnalysis", documents, {
  includeOpinionMining: true,
});

for (const result of results) {
  if (result.error) {
    // a document has an error instead of results
  } else {
    const { sentiment, confidenceScores, sentences } = result;
    for (const { sentiment, confidenceScores, opinions } of sentences) {
      for (const { target, assessments } of opinions) {
        const { text, sentiment, confidenceScores } = target;
        for (const { text, sentiment } of assessments) {
          // Do something
        }
      }
    }
  }
}

See https://docs.microsoft.com//azure/cognitive-services/language-service/sentiment-opinion-mining/overview for more information on opinion mining.

Personally identifiable information

const documents = [<input strings>];
const languageHint = "en";
const categoriesFilter = [KnownPiiCategory.USSocialSecurityNumber];
const domainFilter = KnownPiiDomain.Phi;
const results = await client.analyze("PiiEntityRecognition", documents, languageHint, {
  domainFilter, categoriesFilter
});

for (const result of results) {
  if (result.error) {
    // a document has an error instead of results
  } else {
    const { entities, redactedText } = result;
    for (const { text, category, confidenceScore, length, offset } of entities) {
      // Do something
    }
  }
}

See https://docs.microsoft.com//azure/cognitive-services/language-service/personally-identifiable-information/overview for more information on personally identifiable information.

function analyze<ActionName>(actionName: ActionName, documents: string[], languageCode?: string, options?: AnalyzeActionParameters<ActionName> & TextAnalysisOperationOptions): Promise<AnalyzeResult<ActionName>>

Parameters

actionName

ActionName

the name of the action to be performed on the input documents, see $AnalyzeActionName

documents

string[]

the input documents to be analyzed

languageCode

string

the code of the language that all the input strings are written in. If unspecified, this value will be set to the default language in TextAnalysisClientOptions. If set to an empty string, the service will apply a model where the language is explicitly set to "None". Language support varies per action, for example, more information about the languages supported for Entity Recognition actions can be found in https://docs.microsoft.com//azure/cognitive-services/language-service/named-entity-recognition/language-support

options

AnalyzeActionParameters<ActionName> & TextAnalysisOperationOptions

optional action parameters and settings for the operation

Returns

Promise<AnalyzeResult<ActionName>>

an array of results corresponding to the input documents

analyze<ActionName>(ActionName, TextDocumentInput[], AnalyzeActionParameters<ActionName> & TextAnalysisOperationOptions)

Runs a predictive model to perform the action of choice on the input documents. See $AnalyzeActionName for a list of supported actions.

The layout of each item in the results array depends on the action chosen. For example, each PIIEntityRecognition document result consists of both entities and redactedText where the former is a list of all Pii entities in the text and the latter is the original text after all such Pii entities have been redacted from it.

See https://docs.microsoft.com//azure/cognitive-services/language-service/concepts/data-limits for data limits.

Examples

Opinion mining

const documents = [{
 id: "1",
 text: "The food and service aren't the best",
 language: "en"
}];
const results = await client.analyze("SentimentAnalysis", documents, {
  includeOpinionMining: true,
});

for (const result of results) {
  if (result.error) {
    // a document has an error instead of results
  } else {
    const { sentiment, confidenceScores, sentences } = result;
    for (const { sentiment, confidenceScores, opinions } of sentences) {
      for (const { target, assessments } of opinions) {
        const { text, sentiment, confidenceScores } = target;
        for (const { text, sentiment } of assessments) {
          // Do something
        }
      }
    }
  }
}

See https://docs.microsoft.com//azure/cognitive-services/language-service/sentiment-opinion-mining/overview for more information on opinion mining.

Personally identifiable information

const documents = [<input documents>];
const categoriesFilter = [KnownPiiCategory.USSocialSecurityNumber];
const domainFilter = KnownPiiDomain.Phi;
const results = await client.analyze("PiiEntityRecognition", documents, {
  domainFilter, categoriesFilter
});

for (const result of results) {
  if (result.error) {
    // a document has an error instead of results
  } else {
    const { entities, redactedText } = result;
    for (const { text, category, confidenceScore, length, offset } of entities) {
      // Do something
    }
  }
}

See https://docs.microsoft.com//azure/cognitive-services/language-service/personally-identifiable-information/overview for more information on personally identifiable information.

function analyze<ActionName>(actionName: ActionName, documents: TextDocumentInput[], options?: AnalyzeActionParameters<ActionName> & TextAnalysisOperationOptions): Promise<AnalyzeResult<ActionName>>

Parameters

actionName

ActionName

the name of the action to be performed on the input documents, see $AnalyzeActionName

documents

TextDocumentInput[]

the input documents to be analyzed

options

AnalyzeActionParameters<ActionName> & TextAnalysisOperationOptions

optional action parameters and settings for the operation

Returns

Promise<AnalyzeResult<ActionName>>

an array of results corresponding to the input documents

beginAnalyzeBatch(AnalyzeBatchAction[], string[], string, BeginAnalyzeBatchOptions)

Performs an array (batch) of actions on the input documents. Each action has a kind field that specifies the nature of the action. See $AnalyzeBatchActionNames for a list of supported actions. In addition to kind, actions could also have other parameters such as disableServiceLogs and modelVersion.

The results array contains the results for those input actions where each item also has a kind field that specifies the type of the results.

See https://docs.microsoft.com//azure/cognitive-services/language-service/concepts/data-limits for data limits.

Examples

Key phrase extraction and Pii entity recognition

const poller = await client.beginAnalyzeBatch(
 [{ kind: "KeyPhraseExtraction" }, { kind: "PiiEntityRecognition" }],
 documents
);
const actionResults = await poller.pollUntilDone();

for await (const actionResult of actionResults) {
 if (actionResult.error) {
   throw new Error(`Unexpected error`);
 }
 switch (actionResult.kind) {
   case "KeyPhraseExtraction": {
     for (const doc of actionResult.results) {
       // do something
     }
     break;
   }
   case "PiiEntityRecognition": {
     for (const doc of actionResult.results) {
       // do something
     }
     break;
   }
 }
}

function beginAnalyzeBatch(actions: AnalyzeBatchAction[], documents: string[], languageCode?: string, options?: BeginAnalyzeBatchOptions): Promise<AnalyzeBatchPoller>

Parameters

actions

AnalyzeBatchAction[]

an array of actions that will be run on the input documents

documents

string[]

the input documents to be analyzed

languageCode

string

the code of the language that all the input strings are written in. If unspecified, this value will be set to the default language in TextAnalysisClientOptions. If set to an empty string, the service will apply a model where the language is explicitly set to "None". Language support varies per action, for example, more information about the languages supported for Entity Recognition actions can be found in https://docs.microsoft.com//azure/cognitive-services/language-service/named-entity-recognition/language-support

options
BeginAnalyzeBatchOptions

optional settings for the operation

Returns

Promise<AnalyzeBatchPoller>

an array of results corresponding to the input actions

beginAnalyzeBatch(AnalyzeBatchAction[], TextDocumentInput[], BeginAnalyzeBatchOptions)

Performs an array (batch) of actions on the input documents. Each action has a kind field that specifies the nature of the action. See $AnalyzeBatchActionNames for a list of supported actions. In addition to kind, actions could also have other parameters such as disableServiceLogs and modelVersion.

The results array contains the results for those input actions where each item also has a kind field that specifies the type of the results.

See https://docs.microsoft.com//azure/cognitive-services/language-service/concepts/data-limits for data limits.

Examples

Keyphrase extraction and Pii entity recognition

const poller = await client.beginAnalyzeBatch(
 [{ kind: "KeyPhraseExtraction" }, { kind: "PiiEntityRecognition" }],
 documents
);
const actionResults = await poller.pollUntilDone();

for await (const actionResult of actionResults) {
 if (actionResult.error) {
   throw new Error(`Unexpected error`);
 }
 switch (actionResult.kind) {
   case "KeyPhraseExtraction": {
     for (const doc of actionResult.results) {
       // do something
     }
     break;
   }
   case "PiiEntityRecognition": {
     for (const doc of actionResult.results) {
       // do something
     }
     break;
   }
 }
}

function beginAnalyzeBatch(actions: AnalyzeBatchAction[], documents: TextDocumentInput[], options?: BeginAnalyzeBatchOptions): Promise<AnalyzeBatchPoller>

Parameters

actions

AnalyzeBatchAction[]

an array of actions that will be run on the input documents

documents

TextDocumentInput[]

the input documents to be analyzed

options
BeginAnalyzeBatchOptions

optional settings for the operation

Returns

Promise<AnalyzeBatchPoller>

an array of results corresponding to the input actions

restoreAnalyzeBatchPoller(string, RestoreAnalyzeBatchPollerOptions)

Creates a poller from the serialized state of another poller. This can be useful when you want to create pollers on a different host or a poller needs to be constructed after the original one is not in scope.

function restoreAnalyzeBatchPoller(serializedState: string, options?: RestoreAnalyzeBatchPollerOptions): Promise<AnalyzeBatchPoller>

Parameters

serializedState

string

the serialized state of another poller. It is the result of poller.toString()

options
RestoreAnalyzeBatchPollerOptions

optional settings for the operation

Example

client.beginAnalyzeBatch returns a promise that will resolve to a poller. The state of the poller can be serialized and used to create another as follows:

const serializedState = poller.toString();
const rehydratedPoller = await client.createAnalyzeBatchPoller(serializedState);
const actionResults = await rehydratedPoller.pollUntilDone();

Returns

Promise<AnalyzeBatchPoller>