Streaming Genesys Cloud LLM Gateway Chat Completions via WebSocket with TypeScript

What You Will Build

• This tutorial builds a TypeScript WebSocket server that proxies streaming chat completions to the Genesys Cloud LLM Gateway REST API while enforcing context limits, PII filtering, and performance tracking.
• It uses the Genesys Cloud /api/v2/ai/llm/chat/completions endpoint with Server-Sent Events converted to WebSocket messages.
• The implementation covers TypeScript with Node.js, the ws library, and native fetch for API communication.

Prerequisites

• OAuth2 client credentials flow with required scopes: ai:llm:write, ai:llm:read
• Genesys Cloud API v2
• Node.js 18.0 or higher
• External dependencies: npm install ws zod tiktoken uuid dotenv
• A configured Genesys Cloud organization with AI Gateway enabled and a provisioned model ID

Authentication Setup

Genesys Cloud requires JWT bearer tokens for all API interactions. The following class handles token acquisition, caching, and automatic refresh before expiration.

import axios from 'axios';
import dotenv from 'dotenv';

dotenv.config();

interface TokenResponse {
  access_token: string;
  token_type: string;
  expires_in: number;
}

class AuthManager {
  private token: string | null = null;
  private expiresAt: number = 0;

  async getAccessToken(): Promise<string> {
    if (this.token && Date.now() < this.expiresAt - 60000) {
      return this.token;
    }

    try {
      const response = await axios.post<TokenResponse>(
        `${process.env.GENESYS_CLOUD_BASE_URL}/login/oauth2/token`,
        new URLSearchParams({
          grant_type: 'client_credentials',
          client_id: process.env.GENESYS_CLOUD_CLIENT_ID!,
          client_secret: process.env.GENESYS_CLOUD_CLIENT_SECRET!,
          scope: 'ai:llm:write ai:llm:read'
        }),
        { 
          headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
          timeout: 5000
        }
      );

      this.token = response.data.access_token;
      this.expiresAt = Date.now() + (response.data.expires_in * 1000);
      return this.token;
    } catch (error) {
      if (axios.isAxiosError(error) && error.response?.status === 401) {
        throw new Error('OAuth credentials are invalid or expired');
      }
      throw new Error('Failed to acquire Genesys Cloud access token');
    }
  }
}

Implementation

Step 1: Payload Construction, Schema Validation & Context Window Limits

The LLM Gateway requires strict payload formatting. This step constructs the request matrix, validates the schema, and enforces model capacity constraints using token counting.

import { z } from 'zod';
import { encoding_for_model } from 'tiktoken';

const MessageSchema = z.object({
  role: z.enum(['system', 'user', 'assistant']),
  content: z.string().max(4096)
});

interface LLMRequest {
  modelId: string;
  messages: z.infer<typeof MessageSchema>[];
  temperature: number;
}

const MAX_CONTEXT_TOKENS = 8192;
const MODEL_CAPACITY_MAP: Record<string, number> = {
  'gpt-4-1106-preview': 128000,
  'gpt-3.5-turbo-16k': 16385
};

export function validateAndCountTokens(request: LLMRequest): { valid: boolean; tokenCount: number; error?: string } {
  const validation = z.object({
    modelId: z.string().min(1),
    messages: z.array(MessageSchema).min(1),
    temperature: z.number().min(0).max(2)
  }).safeParse(request);

  if (!validation.success) {
    return { valid: false, tokenCount: 0, error: 'Schema validation failed' };
  }

  const modelLimit = MODEL_CAPACITY_MAP[request.modelId] || MAX_CONTEXT_TOKENS;
  const encoder = encoding_for_model('cl100k_base');
  const textMatrix = request.messages.map(m => `${m.role}: ${m.content}`).join('\n');
  const tokenCount = encoder.encode(textMatrix).length;

  if (tokenCount > modelLimit) {
    return { valid: false, tokenCount, error: `Context window exceeded: ${tokenCount} > ${modelLimit}` };
  }

  return { valid: true, tokenCount };
}

Step 2: WebSocket Handshake, Format Verification & Stream Initiation

The handshake operation verifies the incoming WebSocket message format, triggers automatic token counting, and initiates the REST call to Genesys Cloud with exponential backoff for rate limits.

import { WebSocket } from 'ws';
import { validateAndCountTokens } from './validation';

interface HandshakePayload {
  type: 'init';
  payload: LLMRequest;
}

export async function handleHandshake(ws: WebSocket, message: string, authManager: AuthManager): Promise<boolean> {
  try {
    const parsed: HandshakePayload = JSON.parse(message);
    if (parsed.type !== 'init') {
      ws.send(JSON.stringify({ type: 'error', message: 'Invalid handshake type' }));
      return false;
    }

    const validation = validateAndCountTokens(parsed.payload);
    if (!validation.valid) {
      ws.send(JSON.stringify({ type: 'error', message: validation.error }));
      return false;
    }

    ws.send(JSON.stringify({ 
      type: 'handshake_ack', 
      tokenCount: validation.tokenCount,
      modelId: parsed.payload.modelId 
    }));

    const token = await authManager.getAccessToken();
    await initiateStreamWithRetry(ws, parsed.payload, token);
    return true;
  } catch (error) {
    const errorMessage = error instanceof Error ? error.message : 'Handshake failed';
    ws.send(JSON.stringify({ type: 'error', message: errorMessage }));
    return false;
  }
}

async function initiateStreamWithRetry(ws: WebSocket, request: LLMRequest, token: string, retries = 3): Promise<void> {
  for (let attempt = 1; attempt <= retries; attempt++) {
    try {
      const response = await fetch(`${process.env.GENESYS_CLOUD_BASE_URL}/api/v2/ai/llm/chat/completions`, {
        method: 'POST',
        headers: {
          'Authorization': `Bearer ${token}`,
          'Content-Type': 'application/json',
          'Accept': 'text/event-stream'
        },
        body: JSON.stringify({
          model: request.modelId,
          messages: request.messages,
          temperature: request.temperature,
          stream: true
        })
      });

      if (response.status === 429) {
        const retryAfter = response.headers.get('Retry-After') || Math.pow(2, attempt);
        console.log(`Rate limited. Retrying in ${retryAfter}s...`);
        await new Promise(resolve => setTimeout(resolve, Number(retryAfter) * 1000));
        continue;
      }

      if (!response.ok) {
        const errorText = await response.text();
        throw new Error(`API ${response.status}: ${errorText}`);
      }

      await processStreamResponse(ws, response.body as ReadableStream);
      return;
    } catch (error) {
      if (attempt === retries) {
        ws.send(JSON.stringify({ type: 'error', message: 'Stream initiation failed after retries' }));
        return;
      }
    }
  }
}

Step 3: PII Filtering, Safety Guardrails & Analytics Callbacks

This step implements the validation pipeline for generated content, synchronizes with external dashboards via callbacks, and tracks latency and token generation rates.

import { v4 as uuidv4 } from 'uuid';

interface AnalyticsCallbacks {
  onLatency: (latencyMs: number) => void;
  onTokenRate: (tokensPerSec: number) => void;
  onAudit: (auditLog: AuditLog) => void;
  onDashboardSync: (event: StreamEvent) => void;
}

interface StreamEvent {
  type: 'chunk' | 'complete' | 'blocked' | 'error';
  timestamp: string;
  data?: unknown;
}

interface AuditLog {
  requestId: string;
  modelId: string;
  timestamp: string;
  status: 'success' | 'failed' | 'blocked';
  tokenCount: number;
  latencyMs: number;
  ppiFiltered: boolean;
  safetyPassed: boolean;
}

function containsPII(text: string): boolean {
  const piiPatterns = [
    /\b\d{3}[-.]?\d{2}[-.]?\d{4}\b/,
    /\b\d{9}\b/,
    /[\w.-]+@[\w.-]+\.\w{2,}/
  ];
  return piiPatterns.some(pattern => pattern.test(text));
}

function passesSafetyGuardrails(text: string): boolean {
  const blockedTerms = ['malicious', 'exploit', 'harmful', 'bypass'];
  return !blockedTerms.some(term => text.toLowerCase().includes(term));
}

export async function processStreamResponse(ws: WebSocket, stream: ReadableStream, callbacks: AnalyticsCallbacks): Promise<void> {
  const requestId = uuidv4();
  const startTime = Date.now();
  let accumulatedText = '';
  let tokenCount = 0;
  let isBlocked = false;

  const reader = stream.getReader();
  const decoder = new TextDecoder();
  let buffer = '';

  try {
    while (true) {
      const { done, value } = await reader.read();
      if (done) break;

      buffer += decoder.decode(value, { stream: true });
      const lines = buffer.split('\n');
      buffer = lines.pop() || '';

      for (const line of lines) {
        if (!line.startsWith('data: ')) continue;
        const dataStr = line.slice(6);
        if (dataStr === '[DONE]') continue;

        try {
          const parsed = JSON.parse(dataStr);
          const delta = parsed.choices?.[0]?.delta?.content;
          if (!delta) continue;

          accumulatedText += delta;
          tokenCount++;

          const ppiCheck = containsPII(delta);
          const safetyCheck = passesSafetyGuardrails(delta);

          if (ppiCheck || !safetyCheck) {
            isBlocked = true;
            const auditLog: AuditLog = {
              requestId,
              modelId: parsed.model || 'unknown',
              timestamp: new Date().toISOString(),
              status: 'blocked',
              tokenCount,
              latencyMs: Date.now() - startTime,
              ppiFiltered: ppiCheck,
              safetyPassed: safetyCheck
            };
            callbacks.onAudit(auditLog);
            callbacks.onDashboardSync({ type: 'blocked', timestamp: auditLog.timestamp });
            ws.send(JSON.stringify({ type: 'blocked', reason: ppiCheck ? 'PII detected' : 'Safety guardrail violation' }));
            return;
          }

          const event: StreamEvent = { type: 'chunk', timestamp: new Date().toISOString(), data: parsed };
          callbacks.onDashboardSync(event);
          ws.send(JSON.stringify({ type: 'chunk', data: parsed }));
        } catch (parseError) {
          console.error('Stream parse error', parseError);
        }
      }
    }
  } finally {
    const endTime = Date.now();
    const latencyMs = endTime - startTime;
    const durationSec = latencyMs / 1000;
    const tokensPerSec = durationSec > 0 ? tokenCount / durationSec : 0;

    callbacks.onLatency(latencyMs);
    callbacks.onTokenRate(tokensPerSec);

    const auditLog: AuditLog = {
      requestId,
      modelId: 'completed',
      timestamp: new Date().toISOString(),
      status: 'success',
      tokenCount,
      latencyMs,
      ppiFiltered: false,
      safetyPassed: true
    };
    callbacks.onAudit(auditLog);
    callbacks.onDashboardSync({ type: 'complete', timestamp: auditLog.timestamp });
    ws.send(JSON.stringify({ type: 'complete', auditLog }));
  }
}

Complete Working Example

The following module integrates all components into a runnable WebSocket server. Configure environment variables before execution.

import { WebSocketServer } from 'ws';
import { AuthManager } from './auth';
import { handleHandshake } from './handshake';
import { AnalyticsCallbacks } from './stream';

const PORT = process.env.WS_PORT || 8080;
const wss = new WebSocketServer({ port: Number(PORT) });
const authManager = new AuthManager();

const analyticsCallbacks: AnalyticsCallbacks = {
  onLatency: (ms) => console.log(`[Analytics] Latency: ${ms}ms`),
  onTokenRate: (tps) => console.log(`[Analytics] Token Rate: ${tps.toFixed(2)} tokens/sec`),
  onAudit: (log) => console.log(`[Audit] ${log.requestId} | ${log.status} | ${log.tokenCount} tokens`),
  onDashboardSync: (event) => console.log(`[Dashboard] Synced: ${event.type}`)
};

wss.on('connection', (ws) => {
  console.log('New WebSocket connection established');

  ws.on('message', async (data) => {
    const message = data.toString();
    await handleHandshake(ws, message, authManager, analyticsCallbacks);
  });

  ws.on('close', () => console.log('WebSocket connection closed'));
  ws.on('error', (err) => console.error('WebSocket error:', err.message));
});

console.log(`LLM Gateway WebSocket proxy listening on port ${PORT}`);

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Invalid client credentials, expired token, or missing ai:llm:write scope.
• Fix: Verify GENESYS_CLOUD_CLIENT_ID and GENESYS_CLOUD_CLIENT_SECRET in the .env file. Ensure the OAuth client is configured with the correct scopes in the Genesys Cloud admin console.
• Code showing the fix: The AuthManager class automatically refreshes tokens. If the initial request fails, check the credentials against the developer portal.

Error: 429 Too Many Requests

• Cause: Exceeding the Genesys Cloud API rate limit for LLM completions.
• Fix: Implement exponential backoff. The initiateStreamWithRetry function already handles this by reading the Retry-After header and delaying subsequent attempts.
• Code showing the fix:

  if (response.status === 429) {
    const retryAfter = response.headers.get('Retry-After') || Math.pow(2, attempt);
    await new Promise(resolve => setTimeout(resolve, Number(retryAfter) * 1000));
    continue;
  }
  

Error: Context Window Exceeded

• Cause: Message history matrix exceeds the model capacity constraint or maximum context window limit.
• Fix: Implement a sliding window or truncation strategy before sending the payload. The validateAndCountTokens function returns the exact token count to help implement truncation logic.
• Code showing the fix:

  const validation = validateAndCountTokens(request);
  if (!validation.valid) {
    // Implement message truncation here before retrying
    const truncatedMessages = request.messages.slice(-5);
    // Re-validate and resend
  }
  

Error: WebSocket Connection Refused

• Cause: Port binding conflict or firewall restrictions blocking inbound traffic on the configured port.
• Fix: Verify the port is available using netstat -an | grep <PORT>. Ensure network security groups allow inbound traffic on the specified port.

Official References

• Genesys Cloud LLM Gateway API Documentation
• Genesys Cloud OAuth 2.0 Client Credentials Flow
• Node.js WebSocket Library (ws)
• Tiktoken Tokenization for Context Windows