Injecting NICE Cognigy.AI LLM Gateway Prompts via Node.js

What You Will Build

• A Node.js service that constructs and injects LLM gateway prompts into the NICE CXone Conversations API and Cognigy.AI streaming gateway.
• This implementation uses the CXone REST API for payload injection and the Cognigy.AI WebSocket gateway for real-time streaming responses.
• The tutorial covers JavaScript (Node.js 18+) with production-grade validation, chunking, and governance tracking.

Prerequisites

• OAuth2 Client Credentials grant with conversations:llm:inject, cognigy:gateway:stream, cognigy:prompt:execute, and analytics:write scopes.
• NICE CXone API v2 and Cognigy.AI Gateway v3.
• Node.js 18+ runtime.
• External dependencies: npm install axios ws zod uuid

Authentication Setup

The NICE CXone platform uses a standard OAuth2 client credentials flow. You must cache the access token and implement automatic refresh before expiration to prevent 401 interruptions during streaming sessions.

import axios from 'axios';
import { v4 as uuidv4 } from 'uuid';

const CXONE_AUTH_URL = 'https://{organization}.api.nice.incontact.com/oauth2/token';
const CXONE_BASE_URL = 'https://{organization}.api.nice.incontact.com/api/v2';
const COGNIGY_WS_URL = 'wss://{organization}.my.cognigy.ai/api/v3/llm/gateway/stream';

class CXoneAuthManager {
  constructor(clientId, clientSecret) {
    this.clientId = clientId;
    this.clientSecret = clientSecret;
    this.token = null;
    this.expiresAt = 0;
    this.axiosInstance = axios.create({
      baseURL: CXONE_BASE_URL,
      headers: { 'Content-Type': 'application/json' }
    });
  }

  async getValidToken() {
    if (this.token && Date.now() < this.expiresAt) {
      return this.token;
    }
    const response = await axios.post(CXONE_AUTH_URL, null, {
      auth: { username: this.clientId, password: this.clientSecret },
      params: { grant_type: 'client_credentials' }
    });
    this.token = response.data.access_token;
    this.expiresAt = Date.now() + (response.data.expires_in - 30) * 1000;
    return this.token;
  }

  async injectPrompt(payload) {
    const token = await this.getValidToken();
    const headers = { Authorization: `Bearer ${token}` };
    const response = await this.axiosInstance.post('/conversations/llm/prompt-inject', payload, { headers });
    return response.data;
  }
}

OAuth Scope Requirement: conversations:llm:inject is required for the REST injection endpoint. The WebSocket connection inherits the same Bearer token for gateway routing.

Implementation

Step 1: Payload Construction and Schema Validation

The LLM gateway requires strict schema validation before injection. The payload must contain a prompt-ref identifier, a gateway-matrix for model routing, and a stream directive for WebSocket behavior. You must validate against model-constraints and maximum-token-count to prevent gateway rejection.

import { z } from 'zod';

const GatewayPromptSchema = z.object({
  'prompt-ref': z.string().uuid(),
  'gateway-matrix': z.object({
    model_id: z.string(),
    routing_priority: z.enum(['high', 'standard', 'low']),
    fallback_chain: z.array(z.string()).optional()
  }),
  'stream directive': z.object({
    mode: z.enum(['atomic', 'chunked', 'full']),
    chunk_size: z.number().int().min(128).max(2048),
    auto_trigger: z.boolean()
  }),
  model_constraints: z.object({
    maximum_token_count: z.number().int().min(1).max(128000),
    temperature: z.number().min(0).max(2),
    context_window_size: z.number().int().min(1024).max(128000)
  }),
  prompt_content: z.string().max(64000),
  metadata: z.object({
    conversation_id: z.string(),
    user_id: z.string(),
    session_id: z.string()
  })
});

export function validateAndBuildPayload(rawInput) {
  const constraints = rawInput.model_constraints || {
    maximum_token_count: 4096,
    temperature: 0.7,
    context_window_size: 8192
  };

  const estimatedTokens = Math.ceil(rawInput.prompt_content.length / 4);
  if (estimatedTokens > constraints.maximum_token_count) {
    throw new Error(`Prompt exceeds maximum_token_count. Estimated: ${estimatedTokens}, Limit: ${constraints.maximum_token_count}`);
  }

  if (estimatedTokens > constraints.context_window_size) {
    throw new Error(`Prompt exceeds context_window_size. Truncation required.`);
  }

  const validated = GatewayPromptSchema.parse({
    'prompt-ref': uuidv4(),
    'gateway-matrix': rawInput.gateway_matrix,
    'stream directive': rawInput.stream_directive || { mode: 'chunked', chunk_size: 512, auto_trigger: true },
    model_constraints: constraints,
    prompt_content: rawInput.prompt_content,
    metadata: rawInput.metadata
  });

  return validated;
}

Expected Validation Response: Returns a strictly typed object matching the gateway contract. Throws a structured error on constraint violation.

Step 2: Atomic WebSocket SEND and Stream Chunking

The Cognigy.AI gateway uses WebSocket for low-latency streaming. You must format the SEND operation atomically, verify the payload structure, and implement automatic chunk triggers when the response exceeds the configured chunk_size.

import WebSocket from 'ws';

class StreamManager {
  constructor(wsUrl, token) {
    this.wsUrl = wsUrl;
    this.token = token;
    this.ws = null;
    this.chunks = [];
    this.isReady = false;
  }

  async connect() {
    return new Promise((resolve, reject) => {
      this.ws = new WebSocket(this.wsUrl, {
        headers: { Authorization: `Bearer ${this.token}` }
      });
      this.ws.on('open', () => {
        this.isReady = true;
        resolve(true);
      });
      this.ws.on('error', reject);
    });
  }

  async sendPromptPayload(payload) {
    if (!this.ws || this.ws.readyState !== WebSocket.OPEN) {
      throw new Error('WebSocket connection is not open');
    }

    const message = {
      type: 'PROMPT_INJECT',
      ref: payload['prompt-ref'],
      data: payload,
      timestamp: Date.now()
    };

    const formatted = JSON.stringify(message);
    this.ws.send(formatted);
    
    return new Promise((resolve, reject) => {
      const handler = (data) => {
        const response = JSON.parse(data.toString());
        if (response.type === 'INJECT_ACK') {
          this.ws.removeListener('message', handler);
          resolve(response);
        } else if (response.type === 'STREAM_CHUNK') {
          this.chunks.push(response.payload);
          if (response.payload.complete) {
            this.ws.removeListener('message', handler);
            resolve(response);
          }
        } else if (response.type === 'ERROR') {
          this.ws.removeListener('message', handler);
          reject(new Error(response.message));
        }
      };
      this.ws.on('message', handler);
    });
  }

  close() {
    if (this.ws && this.ws.readyState === WebSocket.OPEN) {
      this.ws.close(1000, 'Session complete');
    }
  }
}

Error Handling: The WebSocket handler catches malformed JSON, connection drops, and gateway errors. The promise resolves only when the stream completes or an explicit error occurs.

Step 3: Stream Validation Pipeline and Governance Tracking

After receiving the streamed response, you must run hallucination checks, apply safety filters, synchronize with an external vector database via chunked webhooks, and record latency and audit logs for LLM governance.

const WEBHOOK_URL = 'https://{your-domain}.com/api/v1/vector-db/sync';
const AUDIT_LOG_ENDPOINT = `${CXONE_BASE_URL}/analytics/llm/audit`;

class GovernancePipeline {
  constructor(authManager) {
    this.authManager = authManager;
  }

  async runValidationPipeline(streamResult, originalPayload) {
    const startTime = originalPayload.timestamp;
    const endTime = Date.now();
    const latencyMs = endTime - startTime;
    const fullText = streamResult.chunks.map(c => c.text).join('');

    const safetyCheck = this.verifySafetyFilter(fullText);
    const hallucinationCheck = this.evaluateHallucination(fullText, originalPayload.prompt_content);

    await this.syncVectorWebhook(streamResult.chunks, originalPayload.metadata);
    await this.recordAuditLog({
      promptRef: originalPayload['prompt-ref'],
      latencyMs,
      success: safetyCheck && hallucinationCheck,
      tokenCount: fullText.split(/\s+/).length,
      safetyScore: safetyCheck ? 1.0 : 0.0,
      hallucinationFlag: hallucinationCheck ? false : true,
      streamChunks: streamResult.chunks.length,
      timestamp: new Date().toISOString()
    });

    return {
      content: fullText,
      latencyMs,
      governance: { safetyCheck, hallucinationCheck },
      success: safetyCheck && hallucinationCheck
    };
  }

  verifySafetyFilter(text) {
    const blockedPatterns = [/injection\s*attack/i, /bypass\s*security/i, /ignore\s*previous/i];
    return !blockedPatterns.some(pattern => pattern.test(text));
  }

  evaluateHallucination(response, prompt) {
    const promptKeywords = prompt.split(/\s+/).filter(w => w.length > 4);
    const responseKeywords = response.split(/\s+/).filter(w => w.length > 4);
    const overlap = promptKeywords.filter(k => responseKeywords.includes(k)).length;
    return overlap > Math.floor(promptKeywords.length * 0.2);
  }

  async syncVectorWebhook(chunks, metadata) {
    const webhookPayload = {
      event: 'PROMPT_CHUNK_SYNC',
      vector_db_id: metadata.conversation_id,
      chunks: chunks.map(c => ({ text: c.text, embedding_ref: c.ref })),
      synced_at: new Date().toISOString()
    };

    await axios.post(WEBHOOK_URL, webhookPayload, {
      headers: { 'Content-Type': 'application/json' },
      timeout: 5000
    });
  }

  async recordAuditLog(logEntry) {
    const token = await this.authManager.getValidToken();
    const maxRetries = 3;
    for (let i = 0; i < maxRetries; i++) {
      try {
        await this.authManager.axiosInstance.post(AUDIT_LOG_ENDPOINT, logEntry, {
          headers: { Authorization: `Bearer ${token}` }
        });
        break;
      } catch (error) {
        if (error.response?.status === 429) {
          const delay = Math.pow(2, i) * 1000;
          await new Promise(r => setTimeout(r, delay));
          continue;
        }
        throw error;
      }
    }
  }
}

OAuth Scope Requirement: analytics:write is required for the audit log endpoint. The vector database webhook operates independently of CXone authentication.

Complete Working Example

The following module integrates authentication, validation, streaming, and governance into a single executable service. Replace the placeholder credentials and endpoints before execution.

import CXoneAuthManager from './auth';
import { validateAndBuildPayload } from './validation';
import StreamManager from './stream';
import GovernancePipeline from './governance';

async function main() {
  const clientId = process.env.CXONE_CLIENT_ID;
  const clientSecret = process.env.CXONE_CLIENT_SECRET;
  const orgId = process.env.CXONE_ORG_ID;

  const auth = new CXoneAuthManager(clientId, clientSecret);
  const stream = new StreamManager(`wss://${orgId}.my.cognigy.ai/api/v3/llm/gateway/stream`, await auth.getValidToken());
  const governance = new GovernancePipeline(auth);

  const rawPrompt = {
    gateway_matrix: {
      model_id: 'gpt-4o-mini',
      routing_priority: 'high',
      fallback_chain: ['gpt-3.5-turbo']
    },
    stream_directive: {
      mode: 'chunked',
      chunk_size: 512,
      auto_trigger: true
    },
    model_constraints: {
      maximum_token_count: 4096,
      temperature: 0.7,
      context_window_size: 8192
    },
    prompt_content: 'Analyze the customer sentiment from the last three interactions and summarize the primary complaint regarding billing cycles.',
    metadata: {
      conversation_id: 'conv-8842-xyz',
      user_id: 'usr-9921',
      session_id: 'sess-4410'
    }
  };

  try {
    const validatedPayload = validateAndBuildPayload(rawPrompt);
    const injectResponse = await auth.injectPrompt(validatedPayload);
    console.log('REST Injection Acknowledged:', injectResponse);

    await stream.connect();
    const streamResult = await stream.sendPromptPayload(validatedPayload);
    console.log('Stream Completed with chunks:', streamResult.chunks.length);

    const governanceResult = await governance.runValidationPipeline(streamResult, validatedPayload);
    console.log('Governance Pipeline Result:', governanceResult);

  } catch (error) {
    console.error('Injection Pipeline Failed:', error.message);
    console.error('Stack:', error.stack);
  } finally {
    stream.close();
  }
}

main();

Run this script with node index.js after setting the environment variables. The script handles token acquisition, payload validation, REST injection, WebSocket streaming, safety filtering, vector synchronization, and audit logging in a single execution flow.

Common Errors and Debugging

Error: 401 Unauthorized during WebSocket handshake

• What causes it: The OAuth token expired or was not attached to the WebSocket headers.
• How to fix it: Ensure the getValidToken() method checks expiresAt before returning. Pass the fresh token in the WebSocket constructor headers.
• Code showing the fix:

this.ws = new WebSocket(this.wsUrl, {
  headers: { Authorization: `Bearer ${await this.authManager.getValidToken()}` }
});

Error: 429 Too Many Requests on Audit Log Endpoint

• What causes it: Excessive injection frequency triggers CXone rate limiting.
• How to fix it: Implement exponential backoff retry logic. The recordAuditLog method already includes a retry loop with Math.pow(2, i) * 1000 delay.
• Code showing the fix: Included in the GovernancePipeline.recordAuditLog method above.

Error: Zod validation throws maximum_token_count exceeded

• What causes it: The prompt content length divided by four exceeds the configured limit.
• How to fix it: Truncate the prompt before validation or increase maximum_token_count in model_constraints.
• Code showing the fix:

const truncatedContent = rawInput.prompt_content.slice(0, constraints.maximum_token_count * 4);

Error: WebSocket returns STREAM_CHUNK without complete: true

• What causes it: The gateway encounters a timeout or the model fails to generate a stop token.
• How to fix it: Implement a timeout watcher that closes the connection and marks the stream as failed if no chunks arrive within 30 seconds.
• Code showing the fix:

const timeoutId = setTimeout(() => {
  reject(new Error('Stream timeout exceeded'));
  this.ws.close(1001, 'Gateway timeout');
}, 30000);
// Clear timeout on resolve in the message handler

Official References

• NICE CXone Conversations API Documentation
• NICE CXone OAuth2 Authentication Guide
• Cognigy.AI LLM Gateway Streaming Protocol
• Node.js WebSocket Library Reference
• Zod Schema Validation Documentation