GPT Semantic Cache: Reducing LLM Costs and Latency via Semantic Embedding Caching

Abstract

Large Language Models (LLMs), such as GPT, have revolutionized artificial intelligence by enabling nuanced understanding and generation of human-like text across a wide range of applications. However, the high computational and financial costs associated with frequent API calls to these models present a substantial bottleneck, especially for applications like customer service chatbots that handle repetitive queries. In this paper, we introduce GPT Semantic Cache, a method that leverages semantic caching of query embeddings in in-memory storage (Redis). By storing embeddings of user queries, our approach efficiently identifies semantically similar questions, allowing for the retrieval of pre-generated responses without redundant API calls to the LLM. This technique achieves a notable reduction in operational costs while significantly enhancing response times, making it a robust solution for optimizing LLM-powered applications. Our experiments demonstrate that GPT Semantic Cache reduces API calls by up to 68.8% across various query categories, with cache hit rates ranging from 61.6% to 68.8%. Additionally, the system achieves high accuracy, with positive hit rates exceeding 97%, confirming the reliability of cached responses. This technique not only reduces operational costs, but also improves response times, enhancing the efficiency of LLM-powered applications.