Cache Strategy

What problem does it solve?

This Skill addresses the performance bottlenecks caused by repeated, expensive data computations or fetches by implementing effective caching strategies across Python and Next.js applications.

Core Features & Use Cases

• In-Memory Caching: Utilizes Python's @lru_cache and custom TTLCache for efficient data retrieval within a single process.
• Redis Integration: Implements cache-aside patterns using Redis for shared, network-accessible caching.
• Next.js Fetch Caching: Configures fetch directives (cache, revalidate) for optimal data fetching in Server Components.
• Embedding Caching: Specifically caches expensive vector embedding computations.
• Use Case: When fetching user profile data that doesn't change frequently, cache it in Redis with a 5-minute TTL to reduce database load and improve response times for subsequent requests.

Quick Start

Apply the TTLCache pattern with a max size of 256 and a TTL of 300 seconds to cache API responses.