LLM Cache Patterns

Battle-tested caching strategies that reduce LLM costs by up to 95% in production systems.

🚀 Quick Start

npm install llm-cache-patterns
# or
pip install llm-cache-patterns

const { SemanticCache } = require('llm-cache-patterns');

const cache = new SemanticCache({
  threshold: 0.95,  // Similarity threshold
  ttl: 3600,        // 1 hour TTL
  provider: 'redis'
});

// Before: $0.10 per request
const response = await openai.complete(prompt);

// After: $0.005 per request (95% cache hit rate)
const response = await cache.get(prompt, () => openai.complete(prompt));

📊 Real Production Results

Company	Before	After	Savings	Cache Hit Rate
FinTech Startup	$45K/mo	$3K/mo	93%	89%
E-commerce Platform	$120K/mo	$15K/mo	87%	76%
SaaS Analytics	$28K/mo	$1.8K/mo	94%	91%

🎯 Caching Strategies

1. Exact Match Cache

Simple but effective for repeated queries.

const exactCache = new ExactMatchCache({
  provider: 'memory',  // or 'redis', 'dynamodb'
  maxSize: 10000
});

View implementation →

2. Semantic Cache

Uses embeddings to match similar queries.

const semanticCache = new SemanticCache({
  model: 'text-embedding-ada-002',
  threshold: 0.95,
  vectorDB: 'pinecone'  // or 'weaviate', 'qdrant'
});

View implementation →

3. Template Cache

Caches responses for templated prompts.

const templateCache = new TemplateCache({
  templates: {
    'user-query': 'Answer this question about {topic}: {question}',
    'summary': 'Summarize this {type} document: {content}'
  }
});

View implementation →

4. Sliding Window Cache

Time-based cache with sliding expiration.

const slidingCache = new SlidingWindowCache({
  windowSize: 3600,     // 1 hour
  bucketSize: 60,       // 1 minute buckets
  maxTokensPerWindow: 1000000
});

View implementation →

5. Hierarchical Cache

Multi-level caching for different query types.

const hierarchicalCache = new HierarchicalCache({
  levels: [
    { name: 'hot', ttl: 300, size: 1000 },      // 5 min
    { name: 'warm', ttl: 3600, size: 10000 },   // 1 hour
    { name: 'cold', ttl: 86400, size: 100000 }  // 1 day
  ]
});

View implementation →

🔧 Advanced Patterns

Request Deduplication

Prevent duplicate requests in flight.

const deduper = new RequestDeduplicator({
  timeout: 30000  // 30 second window
});

// Multiple simultaneous requests for same prompt
// Only one API call made
const [r1, r2, r3] = await Promise.all([
  deduper.request(prompt, () => llm.complete(prompt)),
  deduper.request(prompt, () => llm.complete(prompt)),
  deduper.request(prompt, () => llm.complete(prompt))
]);

Cache Warming

Preload cache with common queries.

const warmer = new CacheWarmer({
  schedule: '0 6 * * *',  // Daily at 6 AM
  queries: './common-queries.json',
  batchSize: 100
});

await warmer.warmCache(cache);

Smart Invalidation

Intelligent cache invalidation strategies.

const invalidator = new SmartInvalidator({
  rules: [
    { pattern: /stock price/i, ttl: 60 },        // 1 minute for stock data
    { pattern: /weather/i, ttl: 1800 },          // 30 min for weather
    { pattern: /definition/i, ttl: 604800 }      // 1 week for definitions
  ]
});

📈 Monitoring & Analytics

Track cache performance in production.

const monitor = new CacheMonitor({
  metrics: ['hitRate', 'latency', 'savings'],
  dashboard: 'grafana',
  alerts: {
    hitRate: { below: 0.7 },
    latency: { above: 100 }
  }
});

Key Metrics

• Hit Rate: Percentage of requests served from cache
• Latency: Cache vs API response times
• Cost Savings: Actual $ saved
• Token Usage: Cached vs fresh tokens

🛠️ Implementation Examples

Next.js API Route

// pages/api/ai-complete.js
import { SemanticCache } from 'llm-cache-patterns';

const cache = new SemanticCache({
  provider: process.env.REDIS_URL
});

export default async function handler(req, res) {
  const { prompt } = req.body;
  
  const cached = await cache.get(prompt, async () => {
    return await openai.complete({ prompt });
  });
  
  res.json({
    response: cached.value,
    cached: cached.fromCache,
    savings: cached.fromCache ? '$0.02' : '$0.00'
  });
}

Python FastAPI

from llm_cache_patterns import SemanticCache
import openai

cache = SemanticCache(
    provider="redis",
    threshold=0.95
)

@app.post("/complete")
async def complete(prompt: str):
    result = await cache.get(
        prompt,
        lambda: openai.Completion.create(prompt=prompt)
    )
    
    return {
        "response": result.value,
        "cached": result.from_cache,
        "latency": result.latency_ms
    }

🔒 Security Considerations

• PII Handling: Never cache personally identifiable information
• Encryption: Use encryption at rest for sensitive domains
• Access Control: Implement proper cache key namespacing
• Audit Logging: Track cache access for compliance

📚 Resources

• Blog: Why Your AI Agent Needs a Cache
• Video: Implementing Semantic Cache
• Benchmarks: Cache Provider Comparison

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Running Tests

npm test
npm run benchmark

Current Contributors

• @BinaryBourbon - Author
• @contributor1 - Redis optimizations
• @contributor2 - Python bindings

📄 License

MIT License - see LICENSE for details.

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