Implementing NICE Cognigy.AI LLM Response Caching with Node.js

What You Will Build

• One sentence: The code intercepts outgoing LLM gateway requests, checks a Redis cache for identical prompt and context combinations, returns cached responses when available, and prevents cache stampedes with distributed mutex locks.
• One sentence: This implementation uses the NICE Cognigy.AI REST API v2 LLM gateway endpoint and the ioredis client library.
• One sentence: The tutorial covers Node.js 18+ with native fetch, crypto hashing, and structured error handling.

Prerequisites

• OAuth 2.0 Client Credentials flow with the cognigy:api:access scope for Cognigy.AI tenant authentication
• Cognigy.AI REST API v2 (LLM gateway endpoint: /api/v2/llm/generate)
• Node.js 18.0+ or Node.js 20.x LTS
• Redis 7.0+ running locally or in a managed environment
• External dependencies: ioredis@^5.3, uuid@^9.0 (install via npm install ioredis uuid)

Authentication Setup

Cognigy.AI requires a valid bearer token for all API calls. The following module fetches tokens using the client credentials flow and implements automatic refresh before expiration.

import crypto from 'node:crypto';

const COGNIGY_TENANT = process.env.COGNIGY_TENANT || 'demo';
const COGNIGY_CLIENT_ID = process.env.COGNIGY_CLIENT_ID;
const COGNIGY_CLIENT_SECRET = process.env.COGNIGY_CLIENT_SECRET;
const TOKEN_BASE_URL = `https://${COGNIGY_TENANT}.cognigy.ai/api/v2/oauth`;

class CognigyAuthManager {
  constructor() {
    this.token = null;
    this.expiresAt = 0;
  }

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

    const payload = new URLSearchParams({
      grant_type: 'client_credentials',
      client_id: COGNIGY_CLIENT_ID,
      client_secret: COGNIGY_CLIENT_SECRET,
      scope: 'cognigy:api:access'
    }).toString();

    const response = await fetch(`${TOKEN_BASE_URL}/token`, {
      method: 'POST',
      headers: {
        'Content-Type': 'application/x-www-form-urlencoded',
        'Accept': 'application/json'
      },
      body: payload
    });

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

    const data = await response.json();
    this.token = data.access_token;
    this.expiresAt = Date.now() + (data.expires_in * 1000);
    return this.token;
  }
}

export const cognigyAuth = new CognigyAuthManager();

Implementation

Step 1: Redis Connection & Cache Key Generation

The cache layer requires a stable Redis connection and a deterministic cache key derived from the prompt template and context variables. Context variables are sorted alphabetically to guarantee identical keys regardless of insertion order.

import Redis from 'ioredis';
import crypto from 'node:crypto';

const redis = new Redis(process.env.REDIS_URL || 'redis://localhost:6379', {
  maxRetriesPerRequest: 3,
  retryStrategy: (times) => Math.min(times * 50, 2000)
});

redis.on('error', (err) => {
  console.error('Redis connection error:', err.message);
});

export function generateCacheKey(template, contextVariables) {
  const normalizedContext = Object.fromEntries(
    Object.entries(contextVariables || {}).sort((a, b) => a[0].localeCompare(b[0]))
  );
  
  const rawPayload = JSON.stringify({
    template: template,
    context: normalizedContext
  });
  
  return `cognigy:llm:cache:${crypto.createHash('sha256').update(rawPayload).digest('hex')}`;
}

export { redis };

Step 2: Mutex Lock & Stampede Prevention

Cache stampedes occur when multiple concurrent requests miss the cache simultaneously and all trigger expensive LLM generation. A distributed mutex using Redis SET NX PX prevents duplicate generation. The lock automatically expires to avoid deadlocks.

import { v4 as uuidv4 } from 'uuid';
import { redis } from './cache.js';

export class DistributedMutex {
  constructor(key, ttlMs = 15000) {
    this.lockKey = `cognigy:llm:mutex:${key}`;
    this.ttlMs = ttlMs;
    this.lockId = null;
  }

  async acquire() {
    this.lockId = uuidv4();
    const acquired = await redis.set(this.lockKey, this.lockId, 'EX', Math.ceil(this.ttlMs / 1000), 'NX');
    return acquired === 'OK';
  }

  async release() {
    if (!this.lockId) return false;
    const script = `
      if redis.call("get", KEYS[1]) == ARGV[1] then
        return redis.call("del", KEYS[1])
      else
        return 0
      end
    `;
    const result = await redis.eval(script, 1, this.lockKey, this.lockId);
    this.lockId = null;
    return result === 1;
  }

  async withLock(executor) {
    const acquired = await this.acquire();
    if (!acquired) {
      throw new Error('Mutex acquisition failed. Cache stampede prevention active.');
    }
    try {
      return await executor();
    } finally {
      await this.release();
    }
  }
}

Step 3: LLM Gateway Interception & Fallback Logic

This step wraps the Cognigy.AI LLM gateway call. It checks the cache, acquires a mutex on miss, validates the cache again inside the lock (double-check pattern), falls back to live generation on true miss, stores the result with a configurable TTL, and handles 429 rate limits with exponential backoff.

import { redis, generateCacheKey } from './cache.js';
import { DistributedMutex } from './mutex.js';
import { cognigyAuth } from './auth.js';

const LLM_BASE_URL = `https://${process.env.COGNIGY_TENANT || 'demo'}.cognigy.ai/api/v2/llm`;
const DEFAULT_TTL_SECONDS = 300;
const RETRY_BASE_DELAY = 1000;
const MAX_RETRIES = 3;

async function exponentialBackoff(attempt) {
  const delay = RETRY_BASE_DELAY * Math.pow(2, attempt) + Math.random() * 100;
  await new Promise(resolve => setTimeout(resolve, delay));
}

export async function generateLLMResponse(template, contextVariables, ttlSeconds = DEFAULT_TTL_SECONDS) {
  const cacheKey = generateCacheKey(template, contextVariables);
  
  const cached = await redis.get(cacheKey);
  if (cached) {
    return JSON.parse(cached);
  }

  const mutex = new DistributedMutex(cacheKey, 15000);
  
  try {
    return await mutex.withLock(async () => {
      const doubleCheck = await redis.get(cacheKey);
      if (doubleCheck) {
        return JSON.parse(doubleCheck);
      }

      const token = await cognigyAuth.getAccessToken();
      const requestBody = JSON.stringify({
        model: process.env.LLM_MODEL || 'gpt-4',
        prompt: template,
        context: contextVariables || {},
        max_tokens: 1024,
        temperature: 0.7
      });

      let lastError;
      for (let attempt = 0; attempt <= MAX_RETRIES; attempt++) {
        const response = await fetch(`${LLM_BASE_URL}/generate`, {
          method: 'POST',
          headers: {
            'Authorization': `Bearer ${token}`,
            'Content-Type': 'application/json',
            'Accept': 'application/json'
          },
          body: requestBody
        });

        if (response.status === 429 && attempt < MAX_RETRIES) {
          await exponentialBackoff(attempt);
          continue;
        }

        if (!response.ok) {
          const errorBody = await response.text();
          throw new Error(`LLM gateway returned ${response.status}: ${errorBody}`);
        }

        const result = await response.json();
        await redis.set(cacheKey, JSON.stringify(result), 'EX', ttlSeconds);
        return result;
      }

      throw lastError || new Error('LLM generation failed after retries');
    });
  } catch (error) {
    if (error.message.includes('Mutex acquisition failed')) {
      await new Promise(resolve => setTimeout(resolve, 500));
      return JSON.parse(await redis.get(cacheKey));
    }
    throw error;
  }
}

Step 4: Metrics Tracking & Capacity Planning

Capacity planning requires visibility into cache efficiency and Redis memory consumption. This module tracks hit rates, request volumes, and polls Redis INFO memory to report current utilization.

import { redis } from './cache.js';

class LLMMetricsCollector {
  constructor() {
    this.hits = 0;
    this.misses = 0;
    this.errors = 0;
    this.totalRequests = 0;
  }

  recordHit() {
    this.hits++;
    this.totalRequests++;
  }

  recordMiss() {
    this.misses++;
    this.totalRequests++;
  }

  recordError() {
    this.errors++;
    this.totalRequests++;
  }

  getHitRate() {
    if (this.totalRequests === 0) return 0;
    return (this.hits / this.totalRequests) * 100;
  }

  async getMemoryUsage() {
    const info = await redis.info('memory');
    const usedBytesMatch = info.match(/used_memory:(\d+)/);
    const peakBytesMatch = info.match(/used_memory_peak:(\d+)/);
    const maxBytesMatch = info.match(/maxmemory:(\d+)/);
    
    const usedBytes = parseInt(usedBytesMatch?.[1] || '0', 10);
    const peakBytes = parseInt(peakBytesMatch?.[1] || '0', 10);
    const maxBytes = parseInt(maxBytesMatch?.[1] || '0', 10);
    
    return {
      usedBytes,
      peakBytes,
      maxBytes,
      utilizationPercent: maxBytes > 0 ? (usedBytes / maxBytes) * 100 : 0
    };
  }

  report() {
    return {
      totalRequests: this.totalRequests,
      hits: this.hits,
      misses: this.misses,
      errors: this.errors,
      hitRatePercent: parseFloat(this.getHitRate().toFixed(2)),
      timestamp: new Date().toISOString()
    };
  }
}

export const llmMetrics = new LLMMetricsCollector();

Complete Working Example

The following script combines authentication, caching, mutex protection, LLM interception, and metrics tracking into a single runnable module. Replace environment variables with your tenant credentials and Redis connection string.

import { redis, generateCacheKey } from './cache.js';
import { DistributedMutex } from './mutex.js';
import { cognigyAuth } from './auth.js';
import { llmMetrics } from './metrics.js';

const COGNIGY_TENANT = process.env.COGNIGY_TENANT || 'demo';
const LLM_BASE_URL = `https://${COGNIGY_TENANT}.cognigy.ai/api/v2/llm`;
const DEFAULT_TTL_SECONDS = 300;

async function callLLMWithCache(template, contextVariables, ttlSeconds = DEFAULT_TTL_SECONDS) {
  llmMetrics.recordMiss();
  const cacheKey = generateCacheKey(template, contextVariables);
  
  const cached = await redis.get(cacheKey);
  if (cached) {
    llmMetrics.recordHit();
    llmMetrics.recordMiss(); // Adjust counter to reflect actual miss->hit flow
    return JSON.parse(cached);
  }

  const mutex = new DistributedMutex(cacheKey, 15000);
  
  try {
    const result = await mutex.withLock(async () => {
      const doubleCheck = await redis.get(cacheKey);
      if (doubleCheck) {
        llmMetrics.recordHit();
        llmMetrics.recordMiss();
        return JSON.parse(doubleCheck);
      }

      const token = await cognigyAuth.getAccessToken();
      const requestBody = JSON.stringify({
        model: process.env.LLM_MODEL || 'gpt-4',
        prompt: template,
        context: contextVariables || {},
        max_tokens: 1024,
        temperature: 0.7
      });

      let lastError;
      for (let attempt = 0; attempt <= 3; attempt++) {
        const response = await fetch(`${LLM_BASE_URL}/generate`, {
          method: 'POST',
          headers: {
            'Authorization': `Bearer ${token}`,
            'Content-Type': 'application/json',
            'Accept': 'application/json'
          },
          body: requestBody
        });

        if (response.status === 429 && attempt < 3) {
          const delay = 1000 * Math.pow(2, attempt) + Math.random() * 100;
          await new Promise(resolve => setTimeout(resolve, delay));
          continue;
        }

        if (!response.ok) {
          const errorBody = await response.text();
          throw new Error(`LLM gateway returned ${response.status}: ${errorBody}`);
        }

        const result = await response.json();
        await redis.set(cacheKey, JSON.stringify(result), 'EX', ttlSeconds);
        return result;
      }
      throw lastError || new Error('LLM generation failed after retries');
    });

    llmMetrics.recordHit();
    return result;
  } catch (error) {
    if (error.message.includes('Mutex acquisition failed')) {
      await new Promise(resolve => setTimeout(resolve, 500));
      const fallback = await redis.get(cacheKey);
      llmMetrics.recordHit();
      return fallback ? JSON.parse(fallback) : null;
    }
    llmMetrics.recordError();
    throw error;
  }
}

export { callLLMWithCache, llmMetrics };

Common Errors & Debugging

Error: 401 Unauthorized

• What causes it: The OAuth token has expired, the client credentials are incorrect, or the cognigy:api:access scope is missing.
• How to fix it: Verify COGNIGY_CLIENT_ID and COGNIGY_CLIENT_SECRET match the Cognigy.AI API key configuration. Ensure the token endpoint returns a valid access_token. The CognigyAuthManager automatically refreshes tokens, but initial credential mismatches will fail immediately.
• Code showing the fix: The authentication module throws a descriptive error with the raw response body. Log the error and rotate credentials if the tenant was recreated.

Error: 429 Too Many Requests

• What causes it: Cognigy.AI enforces rate limits per tenant or per API key. Concurrent LLM calls exceed the allowed throughput.
• How to fix it: The implementation includes exponential backoff with jitter. Increase RETRY_BASE_DELAY or implement a token bucket rate limiter if your tenant limit is lower than 10 requests per second.
• Code showing the fix: The retry loop in callLLMWithCache catches 429 status codes and delays subsequent attempts using Math.pow(2, attempt).

Error: Redis Connection Refused or ECONNRESET

• What causes it: The Redis server is unreachable, the port is blocked, or the REDIS_URL contains an incorrect protocol.
• How to fix it: Verify Redis is running on the specified host and port. Use redis-cli ping to confirm connectivity. The ioredis configuration includes a retry strategy that backs off on transient network failures.
• Code showing the fix: The redis instance logs connection errors. Add a health check endpoint that calls redis.ping() to detect outages before requests fail.

Error: Mutex Acquisition Failed

• What causes it: Another request holds the lock for the same cache key, indicating active stampede prevention.
• How to fix it: This is expected behavior. The code catches this error, waits 500 milliseconds, and reads the cache again. If the cache remains empty after the wait, increase the mutex TTL or review concurrent request patterns.
• Code showing the fix: The catch block checks error.message.includes('Mutex acquisition failed') and performs a delayed cache read before rethrowing.

Official References

• Cognigy.AI REST API v2 Documentation
• Cognigy.AI OAuth 2.0 Token Endpoint
• ioredis Documentation
• Node.js Crypto Module
• Redis SET Command with NX/PX Options