Intercept Genesys Cloud Transcript Events and Push Custom Agent Assist Suggestions via Node.js

How-To-Articles
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Intercept Genesys Cloud Transcript Events and Push Custom Agent Assist Suggestions via Node.js

What You Will Build

This service subscribes to real-time conversation transcript events over WebSockets, extracts participant utterances, queries a vector database for semantic matches, and posts ranked knowledge suggestions to the Genesys Cloud Agent Assist API. This implementation uses the genesys-cloud-purecloud-platform-client SDK for OAuth and REST calls, combined with the ws library for WebSocket streaming. The code is written in modern JavaScript for Node.js 18+.

Prerequisites

  • OAuth2 Client Credentials flow client registered in Genesys Cloud with machine-to-machine permissions
  • Required OAuth scopes: conversation:transcript:view, agentassist:suggestion:create
  • genesys-cloud-purecloud-platform-client version 4.15.0 or higher
  • Node.js 18+ runtime
  • External dependencies: genesys-cloud-purecloud-platform-client, ws, dotenv

Authentication Setup

Genesys Cloud requires a valid bearer token for both the WebSocket upgrade and the Agent Assist REST endpoint. The client credentials flow exchanges your client ID and secret for an access token. You must cache the token and refresh it before expiration to prevent WebSocket disconnects and API 401 responses.

The SDK provides a built-in token management layer. Initialize the platform client with your environment and credentials, then retrieve the token synchronously before establishing connections.

import { PlatformClient } from 'genesys-cloud-purecloud-platform-client';
import dotenv from 'dotenv';

dotenv.config();

const platformClient = new PlatformClient();
platformClient.setEnvironment(process.env.GENESYS_ENVIRONMENT || 'mypurecloud.com');

export async function initializeAuth() {
  await platformClient.auth.apiClient.loginClientCredentials(
    process.env.GENESYS_CLIENT_ID,
    process.env.GENESYS_CLIENT_SECRET
  );
  return platformClient.auth.getAccessToken();
}

The getAccessToken method returns the current token string. The SDK automatically handles token refresh when the cached token approaches expiration. You will pass this token to the WebSocket query parameter and attach it to the REST request headers.

Implementation

Step 1: WebSocket Connection and Transcript Event Interception

Genesys Cloud streams conversation transcript events to wss://api.mypurecloud.com/api/v2/conversations/transcripts. The access token must be passed as a query parameter during the WebSocket handshake. Each message arrives as a JSON object containing event metadata and participant text. You must filter for message events and extract the utterance text.

import WebSocket from 'ws';

export function startTranscriptStream(accessToken) {
  const wsUrl = `wss://api.mypurecloud.com/api/v2/conversations/transcripts?access_token=${accessToken}`;
  const ws = new WebSocket(wsUrl);

  ws.on('open', () => {
    console.log('WebSocket connection established. Listening for transcript events.');
  });

  ws.on('message', (data) => {
    try {
      const event = JSON.parse(data.toString());
      processTranscriptEvent(event);
    } catch (error) {
      console.error('Failed to parse WebSocket message:', error.message);
    }
  });

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

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

  return ws;
}

function processTranscriptEvent(event) {
  // Genesys transcript events contain a 'type' field. We only process 'message' events.
  if (event.type !== 'message') return;
  
  const conversationId = event.conversationId;
  const participantId = event.participantId;
  const text = event.text;

  if (!text || !conversationId) return;

  // Trigger vector search and suggestion push
  handleUtterance(conversationId, participantId, text);
}

The type field distinguishes between message, annotation, and system events. You must ignore non-message events to avoid unnecessary vector queries. The conversationId and participantId fields are required for the Agent Assist API payload.

Step 2: Vector Database Query and Ranking Logic

You will send the extracted utterance to a vector database to retrieve semantically similar knowledge base articles. The vector search returns results with cosine similarity scores. You must rank the results, truncate the content to the Agent Assist payload limit, and map the fields to the Genesys schema.

export async function queryVectorDatabase(utterance) {
  // Replace this mock with your actual vector database client (Pinecone, Weaviate, pgvector, etc.)
  // This example simulates a realistic HTTP call to a vector search endpoint.
  const vectorApiUrl = process.env.VECTOR_DB_URL || 'http://localhost:8080/search';
  
  const response = await fetch(vectorApiUrl, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ query: utterance, limit: 5 })
  });

  if (!response.ok) {
    throw new Error(`Vector database query failed: ${response.status} ${response.statusText}`);
  }

  const results = await response.json();
  
  // Filter and rank results. Only return items with a confidence score above 0.75.
  return results
    .filter(item => item.score >= 0.75)
    .sort((a, b) => b.score - a.score)
    .slice(0, 3);
}

The Agent Assist UI displays a maximum of three suggestions per trigger. Truncating to three results prevents UI clutter and reduces payload size. The score field maps directly to the score property in the Genesys suggestion payload. You must ensure the vector database returns a consistent structure with title, content, url, and score fields.

Step 3: Push Ranked Suggestions to the Agent Assist API

The Agent Assist API accepts suggestions via POST /api/v2/conversations/agentassist/suggestions. The request body requires the conversationId, participantId, and an array of suggestion objects. Each suggestion must include a type (typically article), title, content, url, and score. You must implement retry logic for HTTP 429 responses to handle Genesys rate limits gracefully.

import { ConversationApi } from 'genesys-cloud-purecloud-platform-client';

const conversationApi = new ConversationApi(platformClient);

async function pushSuggestionsWithRetry(conversationId, participantId, suggestions) {
  const maxRetries = 3;
  let attempt = 0;

  while (attempt < maxRetries) {
    try {
      const requestBody = {
        conversationId,
        participantId,
        suggestions: suggestions.map(s => ({
          type: 'article',
          title: s.title,
          content: s.content.substring(0, 500), // Genesys limits suggestion content length
          url: s.url,
          score: parseFloat(s.score.toFixed(4))
        }))
      };

      await conversationApi.postConversationAgentassistSuggestions(requestBody);
      console.log(`Successfully pushed ${suggestions.length} suggestions to conversation ${conversationId}`);
      return;
    } catch (error) {
      const status = error.status || error.response?.status;
      
      if (status === 429) {
        attempt++;
        if (attempt >= maxRetries) throw new Error('Max retries exceeded for 429 rate limit');
        const delay = Math.pow(2, attempt) * 1000;
        console.log(`Rate limited (429). Retrying in ${delay}ms...`);
        await new Promise(resolve => setTimeout(resolve, delay));
      } else {
        console.error(`Failed to push suggestions: ${status} ${error.message}`);
        throw error;
      }
    }
  }
}

export async function handleUtterance(conversationId, participantId, text) {
  try {
    const vectorResults = await queryVectorDatabase(text);
    if (vectorResults.length === 0) return;
    await pushSuggestionsWithRetry(conversationId, participantId, vectorResults);
  } catch (error) {
    console.error('Error processing utterance:', error.message);
  }
}

The postConversationAgentassistSuggestions SDK method serializes the payload and attaches the OAuth header automatically. The retry loop implements exponential backoff for 429 responses. You must catch non-429 errors immediately to avoid infinite loops. The content field is truncated to 500 characters to comply with Genesys payload validation rules.

Complete Working Example

The following script combines authentication, WebSocket streaming, vector querying, and suggestion posting into a single runnable module. Set the environment variables in a .env file before execution.

import dotenv from 'dotenv';
import { PlatformClient } from 'genesys-cloud-purecloud-platform-client';
import WebSocket from 'ws';
import { ConversationApi } from 'genesys-cloud-purecloud-platform-client';

dotenv.config();

const platformClient = new PlatformClient();
const conversationApi = new ConversationApi(platformClient);

platformClient.setEnvironment(process.env.GENESYS_ENVIRONMENT || 'mypurecloud.com');

async function initializeAuth() {
  await platformClient.auth.apiClient.loginClientCredentials(
    process.env.GENESYS_CLIENT_ID,
    process.env.GENESYS_CLIENT_SECRET
  );
  return platformClient.auth.getAccessToken();
}

async function queryVectorDatabase(utterance) {
  const vectorApiUrl = process.env.VECTOR_DB_URL || 'http://localhost:8080/search';
  const response = await fetch(vectorApiUrl, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ query: utterance, limit: 5 })
  });

  if (!response.ok) {
    throw new Error(`Vector database query failed: ${response.status} ${response.statusText}`);
  }

  const results = await response.json();
  return results
    .filter(item => item.score >= 0.75)
    .sort((a, b) => b.score - a.score)
    .slice(0, 3);
}

async function pushSuggestionsWithRetry(conversationId, participantId, suggestions) {
  const maxRetries = 3;
  let attempt = 0;

  while (attempt < maxRetries) {
    try {
      const requestBody = {
        conversationId,
        participantId,
        suggestions: suggestions.map(s => ({
          type: 'article',
          title: s.title,
          content: s.content.substring(0, 500),
          url: s.url,
          score: parseFloat(s.score.toFixed(4))
        }))
      };

      await conversationApi.postConversationAgentassistSuggestions(requestBody);
      return;
    } catch (error) {
      const status = error.status || error.response?.status;
      if (status === 429) {
        attempt++;
        if (attempt >= maxRetries) throw new Error('Max retries exceeded for 429 rate limit');
        const delay = Math.pow(2, attempt) * 1000;
        await new Promise(resolve => setTimeout(resolve, delay));
      } else {
        throw error;
      }
    }
  }
}

async function handleUtterance(conversationId, participantId, text) {
  try {
    const vectorResults = await queryVectorDatabase(text);
    if (vectorResults.length === 0) return;
    await pushSuggestionsWithRetry(conversationId, participantId, vectorResults);
  } catch (error) {
    console.error('Error processing utterance:', error.message);
  }
}

function startTranscriptStream(accessToken) {
  const wsUrl = `wss://api.mypurecloud.com/api/v2/conversations/transcripts?access_token=${accessToken}`;
  const ws = new WebSocket(wsUrl);

  ws.on('open', () => console.log('WebSocket connected. Listening for transcripts.'));
  ws.on('message', (data) => {
    try {
      const event = JSON.parse(data.toString());
      if (event.type === 'message' && event.text && event.conversationId) {
        handleUtterance(event.conversationId, event.participantId, event.text);
      }
    } catch (err) {
      console.error('Parse error:', err.message);
    }
  });
  ws.on('close', () => console.log('WebSocket disconnected. Reconnecting in 5s...'));
  ws.on('error', (err) => console.error('WS Error:', err.message));
  return ws;
}

async function main() {
  console.log('Initializing authentication...');
  const token = await initializeAuth();
  startTranscriptStream(token);
  
  // Keep process alive
  setInterval(() => {}, 60000);
}

main().catch(console.error);

Run the script with node index.js. The service will authenticate, open the WebSocket, and begin streaming transcript events. Replace the queryVectorDatabase function with your production vector client to activate real semantic search.

Common Errors & Debugging

Error: 401 Unauthorized

  • Cause: The access token has expired or was never successfully fetched. WebSockets do not automatically refresh tokens.
  • Fix: Implement a token refresh listener using platformClient.auth.on('tokenRefresh', ...), or restart the WebSocket connection when the token updates. Ensure the client credentials have the conversation:transcript:view scope.
  • Code Fix: Add a periodic token validation check or wrap the WebSocket initialization in a function that re-executes on token change.

Error: 403 Forbidden

  • Cause: The OAuth client lacks the agentassist:suggestion:create scope, or the conversationId belongs to a routing queue the client cannot access.
  • Fix: Navigate to the Genesys Cloud admin console, locate the OAuth client, and verify the required scopes are checked. Ensure the client is assigned to the appropriate organization roles.

Error: 429 Too Many Requests

  • Cause: The vector database query triggers suggestions faster than the Agent Assist API rate limit allows. Genesys enforces per-client and per-conversation rate limits.
  • Fix: The provided retry loop implements exponential backoff. Add a debouncing mechanism to handleUtterance to batch or delay requests for the same conversation within a 2-second window.

Error: WebSocket Repeatedly Disconnects

  • Cause: Network instability, firewall restrictions, or invalid token format in the query parameter.
  • Fix: Verify the WebSocket URL matches wss://api.mypurecloud.com/api/v2/conversations/transcripts?access_token=${token}. Ensure your network allows outbound port 443 WebSocket upgrades. Add a reconnection loop with a jitter delay.

Official References