Building a cost-efficient LLM caching layer in Python

LLM API costs add up fast. If your application calls a language model API for every user request, you are paying for a lot of duplicate work. In many production systems, 30–50% of incoming queries are either exact repeats or semantically near-identical to something you have already answered. A caching layer captures those hits before they reach the API. This tutorial builds a two-tier cache: exact-match via Redis (SHA-256 key) and semantic near-duplicate detection via cosine similarity over sto...