This tutorial introduces a semantic caching technique for rate-limited APIs, implemented in pure Python without external dependencies. The approach reuses responses for semantically similar queries, unlike exact caching which fails on paraphrased questions. By using character n-grams and cosine similarity, the cache can reduce load and conserve API quota, with a configurable threshold to balance precision and recall. AI
IMPACT Reduces API costs and latency for LLM applications by intelligently reusing responses.
RANK_REASON Tutorial on implementing a specific software tool/technique.
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