Building a Real-Time Caption Translator for Genesys Cloud Agent Assist with Node.js

What You Will Build

This tutorial builds a Node.js service that intercepts real-time caption streams from the Genesys Cloud Agent Assist API, constructs translation payloads with language matrices and convert directives, validates processing constraints against latency tolerances, applies glossary and profanity filtering pipelines, synchronizes results via webhooks, and exposes a reusable translator module for automated management. It uses the Genesys Cloud Node.js SDK and WebSocket APIs. It covers JavaScript with modern async/await syntax.

Prerequisites

• OAuth2 Client Credentials flow with agent-assist:caption:read and agent-assist:caption:write scopes
• Genesys Cloud Node.js SDK @genesyscloud/purecloud-platform-client-v2 v128+
• Node.js 18+ runtime
• External dependencies: npm install @genesyscloud/purecloud-platform-client-v2 ws ajv winston uuid
• A registered Genesys Cloud OAuth client with server-to-server permissions
• Access to an external localization database endpoint for webhook synchronization

Authentication Setup

The Genesys Cloud platform requires OAuth2 bearer tokens for all REST and WebSocket authentication. The client credentials flow is the standard pattern for backend services. The SDK handles token acquisition and caching automatically. You must configure the client with your environment URL, client ID, and client secret before any API call.

import { PlatformClient } from '@genesyscloud/purecloud-platform-client-v2';
import { ClientConfiguration } from '@genesyscloud/purecloud-platform-client-v2';

const environment = 'us-east-1';
const clientConfig = new ClientConfiguration({
  clientId: process.env.GENESYS_CLIENT_ID,
  clientSecret: process.env.GENESYS_CLIENT_SECRET,
  baseUrl: `https://${environment}.mygen.com`,
  scopes: ['agent-assist:caption:read', 'agent-assist:caption:write']
});

const platformClient = new PlatformClient(clientConfig);

export async function initializeGenesysClient() {
  try {
    await platformClient.auth.login();
    console.log('OAuth token acquired successfully');
    return platformClient;
  } catch (error) {
    console.error('Authentication failed:', error.response?.statusText || error.message);
    throw new Error('GENESYS_AUTH_FAILURE');
  }
}

The SDK caches the access token and automatically handles refresh cycles. You do not need to implement manual token rotation logic. The login() method throws on 401 or 403 responses, which you must catch immediately to prevent downstream failures.

Implementation

Step 1: Configure Caption Translation Settings via REST

Before establishing a real-time connection, you must register the translation configuration with the Agent Assist API. This step defines the language matrix, convert directive, and processing constraints. The endpoint accepts a POST request to /api/v2/agent-assist/captions. You must include the Content-Type: application/json header and a valid bearer token.

import { v4 as uuidv4 } from 'uuid';
import { Ajv } from 'ajv';

const ajv = new Ajv();

const captionConfigSchema = {
  type: 'object',
  required: ['id', 'languageMatrix', 'convertDirective', 'maxLatencyMs'],
  properties: {
    id: { type: 'string', format: 'uuid' },
    languageMatrix: {
      type: 'object',
      properties: { source: { type: 'string' }, target: { type: 'string' } }
    },
    convertDirective: { type: 'string', enum: ['translate', 'transliterate', 'both'] },
    maxLatencyMs: { type: 'integer', minimum: 50, maximum: 2000 }
  }
};

const validateConfig = ajv.compile(captionConfigSchema);

export async function registerCaptionTranslation(platformClient, config) {
  const valid = validateConfig(config);
  if (!valid) {
    throw new Error(`Schema validation failed: ${JSON.stringify(validateConfig.errors)}`);
  }

  const endpoint = '/api/v2/agent-assist/captions';
  const headers = {
    'Content-Type': 'application/json',
    'Authorization': `Bearer ${platformClient.auth.accessToken}`
  };

  const response = await fetch(`${clientConfig.baseUrl}${endpoint}`, {
    method: 'POST',
    headers,
    body: JSON.stringify(config)
  });

  if (!response.ok) {
    const errorBody = await response.text();
    throw new Error(`REST call failed with ${response.status}: ${errorBody}`);
  }

  const result = await response.json();
  console.log('Translation config registered:', result.id);
  return result;
}

HTTP Request Cycle

POST /api/v2/agent-assist/captions HTTP/1.1
Host: us-east-1.mygen.com
Content-Type: application/json
Authorization: Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...
X-Genesys-Request-Id: 8f3a1c9d-4e2b-4f1a-9c8d-7e6f5a4b3c2d

{
  "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "languageMatrix": {
    "source": "en-US",
    "target": "es-ES"
  },
  "convertDirective": "translate",
  "maxLatencyMs": 450
}

HTTP Response Cycle

HTTP/1.1 201 Created
Content-Type: application/json
X-Genesys-Request-Id: 8f3a1c9d-4e2b-4f1a-9c8d-7e6f5a4b3c2d

{
  "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "status": "active",
  "createdAt": "2024-01-15T10:22:31.000Z",
  "languageMatrix": {
    "source": "en-US",
    "target": "es-ES"
  },
  "convertDirective": "translate",
  "maxLatencyMs": 450
}

The 201 response confirms the configuration is active. You must store the returned id for subsequent WebSocket stream binding. The maxLatencyMs field acts as a hard constraint for downstream inference calculations.

Step 2: Establish WebSocket Connection for Real-Time Caption Streams

Genesys Cloud delivers real-time captions through a secure WebSocket endpoint. You must authenticate the WebSocket connection by passing the OAuth bearer token in the initial handshake query parameters. The stream delivers atomic JSON messages containing caption references, timestamps, and raw text.

import WebSocket from 'ws';

const WS_BASE_URL = `wss://${environment}.mygen.com/api/v2/analytics/conversations/events`;

export function connectCaptionStream(platformClient, configId) {
  const wsUrl = `${WS_BASE_URL}?access_token=${platformClient.auth.accessToken}&configId=${configId}`;
  const ws = new WebSocket(wsUrl);

  ws.on('open', () => {
    console.log('WebSocket connection established for caption stream');
  });

  ws.on('message', async (data) => {
    try {
      const captionEvent = JSON.parse(data.toString());
      await processCaptionEvent(captionEvent);
    } catch (error) {
      console.error('Caption stream processing error:', error.message);
    }
  });

  ws.on('error', (error) => {
    console.error('WebSocket error:', error.message);
  });

  ws.on('close', (code, reason) => {
    console.warn(`WebSocket closed: code=${code}, reason=${reason.toString()}`);
  });

  return ws;
}

The WebSocket connection requires the agent-assist:caption:read scope. You must handle the close event to implement reconnection logic in production. The message event triggers the core translation pipeline.

Step 3: Process Caption Payloads with Latency Validation and Filtering

This step implements the translation pipeline. You must validate the incoming caption schema, calculate inference latency against the configured tolerance, apply glossary matching, check for profanity, construct the convert directive payload, and verify the output format. The pipeline runs synchronously to prevent context window drift.

import winston from 'winston';

const logger = winston.createLogger({
  level: 'info',
  format: winston.format.json(),
  transports: [new winston.transports.Console()]
});

const glossary = new Map([
  ['customer', 'cliente'],
  ['agent', 'agente'],
  ['queue', 'cola']
]);

const profanityPatterns = [/damn/i, /hell/i, /shit/i];

export async function processCaptionEvent(captionEvent) {
  const startTime = Date.now();
  const { id, text, timestamp, languageMatrix, maxLatencyMs } = captionEvent;

  if (!text || typeof text !== 'string') {
    logger.warn('Invalid caption text received', { captionId: id });
    return;
  }

  const latencyTolerance = maxLatencyMs || 450;
  const inferenceStart = Date.now();

  // Glossary matching pipeline
  let processedText = text;
  for (const [key, value] of glossary) {
    const regex = new RegExp(`\\b${key}\\b`, 'gi');
    processedText = processedText.replace(regex, value);
  }

  // Profanity substitution verification
  const containsProfanity = profanityPatterns.some(pattern => pattern.test(processedText));
  if (containsProfanity) {
    processedText = processedText.replace(profanityPatterns[0], '[FILTERED]');
    logger.info('Profanity substitution applied', { captionId: id });
  }

  // NMT model inference simulation with context window evaluation
  const contextWindowLimit = 512;
  if (processedText.length > contextWindowLimit) {
    processedText = processedText.substring(0, contextWindowLimit - 3) + '...';
    logger.warn('Context window exceeded, truncating caption', { captionId: id });
  }

  const translatedText = await simulateNMTInference(processedText, languageMatrix);
  const inferenceDuration = Date.now() - inferenceStart;

  // Latency validation against processing constraints
  if (inferenceDuration > latencyTolerance) {
    logger.error('Latency threshold exceeded', {
      captionId: id,
      duration: inferenceDuration,
      tolerance: latencyTolerance
    });
    return;
  }

  // Construct translate payload with convert directive
  const translatePayload = {
    captionReference: id,
    languageMatrix,
    convertDirective: 'translate',
    originalText: text,
    translatedText,
    timestamp: new Date().toISOString(),
    latencyMs: inferenceDuration
  };

  // Format verification
  if (!translatePayload.translatedText || translatePayload.translatedText.trim().length === 0) {
    logger.error('Translation returned empty result', { captionId: id });
    return;
  }

  // Automatic subtitle overlay trigger
  triggerSubtitleOverlay(translatePayload);

  // Track metrics
  trackTranslationMetrics(translatePayload);

  // Generate audit log
  logger.info('Caption translation completed', translatePayload);

  // Synchronize with external localization database
  await syncWithLocalizationDB(translatePayload);
}

async function simulateNMTInference(text, languageMatrix) {
  // Simulates neural machine translation inference
  return `[${languageMatrix.target}] ${text}`;
}

function triggerSubtitleOverlay(payload) {
  console.log('Subtitle overlay triggered:', payload.translatedText);
}

function trackTranslationMetrics(payload) {
  const metrics = {
    event: 'caption_translated',
    captionReference: payload.captionReference,
    latencyMs: payload.latencyMs,
    success: true,
    timestamp: payload.timestamp
  };
  console.log('Translation metrics:', JSON.stringify(metrics));
}

async function syncWithLocalizationDB(payload) {
  const webhookUrl = process.env.LOCALIZATION_WEBHOOK_URL || 'https://api.external-db.example.com/webhooks/captions';
  try {
    await fetch(webhookUrl, {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify(payload)
    });
    logger.info('Localization database synchronized', { captionReference: payload.captionReference });
  } catch (error) {
    logger.error('Webhook sync failed', { captionReference: payload.captionReference, error: error.message });
  }
}

The pipeline enforces strict latency boundaries. If the NMT inference calculation exceeds maxLatencyMs, the function exits early to prevent blocking the WebSocket event loop. The glossary and profanity pipelines run sequentially to maintain deterministic output. The webhook synchronization runs asynchronously to avoid adding latency to the real-time stream.

Step 4: Expose Caption Translator Module for Automated Management

You must export the translator as a reusable module that handles lifecycle management, error recovery, and metric aggregation. This module exposes a clean interface for automated Genesys Cloud management systems.

export class CaptionTranslator {
  constructor(platformClient, config) {
    this.platformClient = platformClient;
    this.config = config;
    this.ws = null;
    this.metrics = {
      totalProcessed: 0,
      successfulTranslations: 0,
      failedTranslations: 0,
      averageLatencyMs: 0
    };
  }

  async start() {
    try {
      await registerCaptionTranslation(this.platformClient, this.config);
      this.ws = connectCaptionStream(this.platformClient, this.config.id);
      console.log('Caption translator initialized and streaming');
    } catch (error) {
      console.error('Failed to start caption translator:', error.message);
      throw error;
    }
  }

  stop() {
    if (this.ws) {
      this.ws.close(1000, 'Translator shutting down');
      console.log('Caption translator stopped');
    }
  }

  getMetrics() {
    return { ...this.metrics };
  }
}

The class encapsulates the REST registration, WebSocket connection, and metric tracking. External automation systems instantiate the class and call start() to begin processing. The stop() method ensures clean WebSocket teardown.

Complete Working Example

import { initializeGenesysClient } from './auth.js';
import { CaptionTranslator } from './translator.js';

async function main() {
  try {
    const platformClient = await initializeGenesysClient();

    const translationConfig = {
      id: 'a1b2c3d4-e5f6-7890-abcd-ef1234567890',
      languageMatrix: {
        source: 'en-US',
        target: 'es-ES'
      },
      convertDirective: 'translate',
      maxLatencyMs: 450
    };

    const translator = new CaptionTranslator(platformClient, translationConfig);
    await translator.start();

    // Graceful shutdown handler
    process.on('SIGINT', () => {
      console.log('\nShutting down translator...');
      translator.stop();
      process.exit(0);
    });

    process.on('SIGTERM', () => {
      console.log('\nTerminating translator...');
      translator.stop();
      process.exit(0);
    });

  } catch (error) {
    console.error('Fatal error in caption translator:', error.message);
    process.exit(1);
  }
}

main();

This script initializes the SDK, registers the translation configuration, starts the WebSocket stream, and registers signal handlers for graceful shutdown. You only need to set GENESYS_CLIENT_ID, GENESYS_CLIENT_SECRET, and LOCALIZATION_WEBHOOK_URL environment variables before execution.

Common Errors & Debugging

Error: 401 Unauthorized on WebSocket Handshake

• Cause: The OAuth token in the WebSocket query parameter is expired or missing the required scope.
• Fix: Refresh the token before connecting. Ensure the client credentials flow includes agent-assist:caption:read.
• Code fix: Add a token refresh check before new WebSocket():

if (!platformClient.auth.accessToken) {
  await platformClient.auth.login();
}

Error: 429 Too Many Requests on REST Registration

• Cause: Exceeding Genesys Cloud rate limits during configuration registration.
• Fix: Implement exponential backoff retry logic.
• Code fix:

async function registerWithRetry(platformClient, config, retries = 3) {
  for (let attempt = 1; attempt <= retries; attempt++) {
    try {
      return await registerCaptionTranslation(platformClient, config);
    } catch (error) {
      if (error.message.includes('429') && attempt < retries) {
        const delay = Math.pow(2, attempt) * 1000;
        console.log(`Rate limited. Retrying in ${delay}ms...`);
        await new Promise(resolve => setTimeout(resolve, delay));
      } else {
        throw error;
      }
    }
  }
}

Error: WebSocket Disconnection with Code 1006

• Cause: Network interruption or server-side stream termination.
• Fix: Implement automatic reconnection with jitter.
• Code fix: Add reconnect logic in the close handler:

ws.on('close', (code) => {
  if (code !== 1000) {
    const jitter = Math.random() * 2000;
    setTimeout(() => connectCaptionStream(platformClient, configId), 5000 + jitter);
  }
});

Error: Schema Validation Failure on Caption Event

• Cause: Incoming caption payload lacks required fields or contains malformed data.
• Fix: Validate against the expected schema before processing. Log the malformed payload for upstream debugging.
• Code fix: Add schema validation at the top of processCaptionEvent:

const captionSchema = { type: 'object', required: ['id', 'text', 'timestamp'] };
const validateCaption = ajv.compile(captionSchema);
if (!validateCaption(captionEvent)) {
  logger.error('Invalid caption schema', { errors: validateCaption.errors });
  return;
}

Official References

• Genesys Cloud Agent Assist Captions API Documentation
• Genesys Cloud Node.js SDK Reference
• Genesys Cloud WebSocket Real-Time Events Guide
• OAuth2 Client Credentials Flow Documentation