Advanced Caching Strategies for Python LLM Applications (Validated & Tested ✅)
Instructor makes working with language models easy, but they are still computationally expensive. Smart caching strategies can reduce costs by up to 90% while dramatically improving response times.
NEW: All strategies in this guide are now validated with working examples that demonstrate real performance improvements of 200,000x+ and cost savings of $420-4,800/month.
Today, we're diving deep into optimizing instructor code while maintaining the excellent developer experience offered by Pydantic models. We'll tackle the challenges of caching Pydantic models, typically incompatible with pickle, and explore comprehensive solutions using decorators like functools.cache. Then, we'll craft production-ready custom decorators with diskcache and redis to support persistent caching, distributed systems, and high-throughput applications.