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[Text Analytics] Update Quickstart - JS
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‎articles/cognitive-services/text-analytics/includes/quickstarts/nodejs-sdk.md

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@@ -51,6 +51,16 @@ npm init
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```
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### Install the client library
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#### [Version 3.0-preview](#tab/version-3)
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Install the `@azure/ai-text-analytics` NPM packages:
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```console
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npm install --save @azure/ai-text-analytics
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```
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#### [Version 2.1](#tab/version-2)
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Install the `@azure/cognitiveservices-textanalytics` NPM packages:
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```console
@@ -61,6 +71,16 @@ Your app's `package.json` file will be updated with the dependencies.
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Create a file named `index.js` and add the following libraries:
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#### [Version 3.0-preview](#tab/version-3)
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```javascript
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"use strict";
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const { TextAnalyticsClient, TextAnalyticsApiKeyCredential } = require("@azure/ai-text-analytics");
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```
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#### [Version 2.1](#tab/version-2)
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```javascript
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"use strict";
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@@ -100,7 +120,7 @@ The response object is a list containing the analysis information for each docum
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Create a new `TextAnalyticsClient` object with your key and endpoint as parameters.
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```javascript
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const client = new TextAnalyticsClient(endpoint, new CognitiveServicesCredential(key));
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const client = new TextAnalyticsClient(endpoint, new TextAnalyticsApiKeyCredential(key));
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```
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#### [Version 2.1](#tab/version-2)
@@ -122,19 +142,19 @@ async function sentimentAnalysis(client){
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const sentimentInput = [
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"I had the best day of my life. I wish you were there with me."
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]
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];
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const sentimentResult = await client.analyzeSentiment(sentimentInput);
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result.forEach(document => {
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sentimentResult.forEach(document => {
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console.log(`ID: ${document.id}`);
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console.log(`\tDocument Sentiment: ${document.sentiment}`);
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console.log(`\tDocument Scores:`);
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console.log(`\t\tPositive: ${document.documentScores.positive.toFixed(2)} \tNegative: ${document.documentScores.negative.toFixed(2)} \tNeutral: ${document.documentScores.neutral.toFixed(2)}`);
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console.log(`\t\tPositive: ${document.sentimentScores.positive.toFixed(2)} \tNegative: ${document.sentimentScores.negative.toFixed(2)} \tNeutral: ${document.sentimentScores.neutral.toFixed(2)}`);
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console.log(`\tSentences Sentiment(${document.sentences.length}):`);
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document.sentences.forEach(sentence => {
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console.log(`\t\tSentence sentiment: ${sentence.sentiment}`)
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console.log(`\t\tSentences Scores:`);
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console.log(`\t\tPositive: ${sentence.sentenceScores.positive.toFixed(2)} \tNegative: ${sentence.sentenceScores.negative.toFixed(2)} \tNeutral: ${sentence.sentenceScores.neutral.toFixed(2)}`);
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console.log(`\t\tPositive: ${sentence.sentimentScores.positive.toFixed(2)} \tNegative: ${sentence.sentimentScores.negative.toFixed(2)} \tNeutral: ${sentence.sentimentScores.neutral.toFixed(2)}`);
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console.log(`\t\tLength: ${sentence.length}, Offset: ${sentence.offset}`);
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})
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});
@@ -150,7 +170,7 @@ Run your code with `node index.js` in your console window.
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ID: 0
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Document Sentiment: positive
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Document Scores:
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Positive: 0.61 Negative: 0.01 Neutral: 0.39
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Positive: 1.00 Negative: 0.00 Neutral: 0.00
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Sentences Sentiment(2):
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Sentence sentiment: positive
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Sentences Scores:
@@ -185,22 +205,18 @@ Run your code with `node index.js` in your console window.
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#### [Version 3.0-preview](#tab/version-3)
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Create an array of strings containing the document you want to analyze. Call the client's `detectLanguages()` method and get the returned `DetectLanguageResult`. Then iterate through the results, and print each document's ID, with respective primary and detected language.
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Create an array of strings containing the document you want to analyze. Call the client's `detectLanguage()` method and get the returned `DetectLanguageResultCollection`. Then iterate through the results, and print each document's ID with respective primary language.
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```javascript
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async function languageDetection(client) {
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const languageInputArray = [
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"Ce document est rédigé en Français."
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]
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const languageResult = await client.detectLanguages(languageInputArray);
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];
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const languageResult = await client.detectLanguage(languageInputArray);
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result.forEach(document => {
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languageResult.forEach(document => {
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console.log(`ID: ${document.id}`);
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document.detectedLanguages.forEach(language =>
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console.log(`\tDetected Language ${language.name}`)
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);
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console.log(`\tPrimary Language ${document.primaryLanguage.name}`)
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});
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}
@@ -213,7 +229,6 @@ Run your code with `node index.js` in your console window.
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```console
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ID: 0
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Detected Language French
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Primary Language French
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```
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@@ -253,13 +268,12 @@ async function entityRecognition(client){
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"Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, to develop and sell BASIC interpreters for the Altair 8800",
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"La sede principal de Microsoft se encuentra en la ciudad de Redmond, a 21 kilómetros de Seattle."
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];
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const entityResults = await client.recognizeEntities(entityInputs);
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entityResults.forEach(document => {
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console.log(`Document ID: ${document.id}`);
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document.entities.forEach(entity => {
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console.log(`\tName: ${entity.text} \tType: ${entity.type} \tSub Type: ${entity.subtype != "" ? entity.subtype : "N/A"}`);
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console.log(`\tName: ${entity.text} \tCategory: ${entity.category} \tSubcategory: ${entity.subCategory ? entity.subCategory : "N/A"}`);
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console.log(`\tOffset: ${entity.offset}, Length: ${entity.length} \tScore: ${entity.score}`);
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});
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});
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```console
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Document ID: 0
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Name: Microsoft Type: Organization Sub Type: N/A
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Name: Microsoft Category: Organization Subcategory: N/A
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Offset: 0, Length: 9 Score: 1
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Name: Bill Gates Type: Person Sub Type: N/A
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Offset: 25, Length: 10 Score: 0.999786376953125
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Name: Paul Allen Type: Person Sub Type: N/A
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Offset: 40, Length: 10 Score: 0.9988105297088623
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Name: April 4, 1975 Type: DateTime Sub Type: Date
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Name: Bill Gates Category: Person Subcategory: N/A
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Offset: 25, Length: 10 Score: 0.67
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Name: Paul Allen Category: Person Subcategory: N/A
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Offset: 40, Length: 10 Score: 0.81
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Name: April 4, 1975 Category: DateTime Subcategory: Date
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Offset: 54, Length: 13 Score: 0.8
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Name: Altair Type: Organization Sub Type: N/A
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Offset: 116, Length: 6 Score: 0.7996330857276917
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Name: 8800 Type: Quantity Sub Type: Number
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Name: interpreters Category: PersonType Subcategory: N/A
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Offset: 95, Length: 12 Score: 0.6
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Name: 8800 Category: Quantity Subcategory: Number
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Offset: 123, Length: 4 Score: 0.8
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Document ID: 1
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Name: Microsoft Type: Organization Sub Type: N/A
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Offset: 21, Length: 9 Score: 0.9837456345558167
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Name: 21 Type: Quantity Sub Type: Number
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Name: Microsoft Category: Organization Subcategory: N/A
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Offset: 21, Length: 9 Score: 0.96
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Name: Redmond Category: Location Subcategory: GPE
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Offset: 60, Length: 7 Score: 0.09
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Name: 21 Category: Quantity Subcategory: Number
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Offset: 71, Length: 2 Score: 0.8
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Name: Seattle Category: Location Subcategory: GPE
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```
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## Using NER to detect personal information
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const entityPiiInput = [
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"Insurance policy for SSN on file 123-12-1234 is here by approved."
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]
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const entityResults = await client.recognizePiiEntities(entityPiiInput);
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];
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const entityPiiResults = await client.recognizePiiEntities(entityPiiInput);
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result.forEach(document => {
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entityPiiResults.forEach(document => {
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console.log(`Document ID: ${document.id}`);
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document.entities.forEach(entity => {
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console.log(`\tName: ${entity.text} \tType: ${entity.type} \tSub Type: ${entity.subtype != "" ? entity.subtype : "N/A"}`);
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console.log(`\tName: ${entity.text} \tCategory: ${entity.category} \tSubcategory: ${entity.subCategory ? entity.subCategory : "N/A"}`);
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console.log(`\tOffset: ${entity.offset}, Length: ${entity.length} \tScore: ${entity.score}`);
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});
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});
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```console
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Document ID: 0
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Name: 123-12-1234 Type: U.S. Social Security Number (SSN) Sub Type: N/A
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Name: 123-12-1234 Category: U.S. Social Security Number (SSN) Subcategory: N/A
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Offset: 33, Length: 11 Score: 0.85
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```
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const linkedEntityInput = [
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"Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, to develop and sell BASIC interpreters for the Altair 8800. During his career at Microsoft, Gates held the positions of chairman, chief executive officer, president and chief software architect, while also being the largest individual shareholder until May 2014."
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]
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];
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const entityResults = await client.recognizeLinkedEntities(linkedEntityInput);
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entityResults.forEach(document => {
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entity.matches.forEach(match => {
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console.log(`\t\tText: ${match.text}`);
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console.log(`\t\tOffset: ${match.offset}, Length: ${match.length} \tScore: ${match.score.toFixed(3)}`);
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})
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});
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});
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});
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}
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Name: Altair 8800 ID: Altair 8800 URL: https://en.wikipedia.org/wiki/Altair_8800 Data Source: Wikipedia
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Matches:
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Text: Altair 8800
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Offset: 116, Length: 11 Score: 0.650
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Offset: 116, Length: 11 Score: 0.777
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Name: Bill Gates ID: Bill Gates URL: https://en.wikipedia.org/wiki/Bill_Gates Data Source: Wikipedia
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Matches:
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Text: Bill Gates
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Offset: 25, Length: 10 Score: 0.243
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Offset: 25, Length: 10 Score: 0.555
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Text: Gates
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Offset: 161, Length: 5 Score: 0.243
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Offset: 161, Length: 5 Score: 0.555
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Name: Paul Allen ID: Paul Allen URL: https://en.wikipedia.org/wiki/Paul_Allen Data Source: Wikipedia
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Matches:
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Text: Paul Allen
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Offset: 40, Length: 10 Score: 0.174
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Offset: 40, Length: 10 Score: 0.533
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Name: Microsoft ID: Microsoft URL: https://en.wikipedia.org/wiki/Microsoft Data Source: Wikipedia
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Matches:
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Text: Microsoft
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Offset: 0, Length: 9 Score: 0.196
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Offset: 0, Length: 9 Score: 0.469
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Text: Microsoft
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Offset: 150, Length: 9 Score: 0.196
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Offset: 150, Length: 9 Score: 0.469
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Name: April 4 ID: April 4 URL: https://en.wikipedia.org/wiki/April_4 Data Source: Wikipedia
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Matches:
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Text: April 4
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Offset: 54, Length: 7 Score: 0.137
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Offset: 54, Length: 7 Score: 0.248
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Name: BASIC ID: BASIC URL: https://en.wikipedia.org/wiki/BASIC Data Source: Wikipedia
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Matches:
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Text: BASIC
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Offset: 89, Length: 5 Score: 0.052
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Offset: 89, Length: 5 Score: 0.281
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```
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#### [Version 2.1](#tab/version-2)
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const keyPhrasesInput = [
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"My cat might need to see a veterinarian.",
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]
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const result = await client.extractKeyPhrases(keyPhrasesInput)
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result.forEach(document => {
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];
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const keyPhraseResult = await client.extractKeyPhrases(keyPhrasesInput);
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keyPhraseResult.forEach(document => {
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console.log(`ID: ${document.id}`);
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console.log(`\tDocument Key Phrases: ${document.keyPhrases}`);
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});

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