This content originally appeared on DEV Community and was authored by Jaron
What I Built
NotGPT – A Premium AI Service with Specialized Personas that are fine-tuned for specific domains. Our platform features intelligent AI personas with semantic memory, real-time response comparison, and advanced conversation management powered by Redis 8’s AI-focused capabilities.
Demo
NOTGPT.net – Experience our AI personas with real-time semantic memory and intelligent response caching.
How I Used Redis 8
We leveraged Redis 8 as our intelligent real-time data layer with several AI-focused implementations:
Distributed Rate Limiting with AI Workload Management
- Multi-tier rate limiting for free, premium, and admin users with Redis-backed sliding windows
- Intelligent fallback system that gracefully degrades to in-memory caching when Redis is unavailable
- Production-grade circuit breaker pattern for handling high AI workload spikes
Semantic Memory Caching
- Real-time persona memory injection with Redis caching for frequently accessed semantic memories
- Context-aware caching that reduces LLM API calls by storing semantically similar conversation contexts
- Dynamic memory invalidation when personas learn new information or receive corrections
Response Comparison Optimization
- Intelligent caching for our ResponseComparisonWidget that compares multiple AI model responses
- Session-based comparison state shared across multiple server instances
- Real-time performance metrics cached in Redis for instant comparison analytics
Multi-Instance Session Management
- Distributed conversation state allowing users to seamlessly continue conversations across different server instances
- Real-time persona selection and configuration sharing
- Session persistence that survives server restarts and deployments
Advanced Fallback Architecture
- Hybrid caching strategy using Redis as primary with intelligent memory-based fallbacks
- Health monitoring with automatic Redis reconnection and failure detection
- Zero-downtime deployments with graceful degradation patterns
Production Deployment:
- Vercel KV integration for serverless Redis deployment
- Environment-based configuration supporting multiple Redis providers
- Comprehensive error handling with detailed logging and monitoring
- Redis 8 enables our AI personas to maintain context, learn from interactions, and provide intelligent responses while ensuring scalability and reliability across our distributed architecture.
Key Technical Implementation:
// Multi-tier rate limiting with Redis
class RateLimiter {
async checkLimit(userId: string, tier: 'free' | 'premium' | 'admin') {
const key = `rate_limit:${userId}:${tier}`
const current = await redis.incr(key)
if (current === 1) await redis.expire(key, 3600)
return current <= this.limits[tier]
}
}
// Semantic memory caching
const memoryKey = `formatted_memories:${personaId}:${userId}`
await redis.setex(memoryKey, 1800, JSON.stringify(formattedMemories))`
This content originally appeared on DEV Community and was authored by Jaron