Build a Dependency-Free Semantic Cache in Python to Cut API Rate-Limit Hits

A tutorial published on DEV Community demonstrates how to build a semantic cache in pure Python without external libraries or a vector database. The cache uses character n-grams and cosine similarity to match semantically equivalent queries, allowing paraphrased questions to reuse stored responses instead of triggering fresh API calls. Unlike exact-match caching, this approach recognises that prompts like 'explain retries' and 'explain retries please' are functionally identical, reducing redundant network requests. Cached entries, including the query, its n-gram vector, and the API response, are persisted to a local JSON file for reuse across sessions. A configurable similarity threshold controls the precision-recall tradeoff, and the guide advises building a small labelled test set to calibrate the threshold before deployment.