What is semantic caching?

Semantic caching is a technique designed to optimize the efficiency and cost-effectiveness of large language model (LLM) APIs by identifying and reusing responses to semantically equivalent queries, thus avoiding redundant API calls. Unlike traditional caching that relies on exact query matches, semantic caching utilizes vector embeddings to capture the meaning of queries and applies similarity thresholds to determine equivalence, potentially reducing API calls by up to 68.8% and improving response latency by 40-50%. However, implementing semantic caching requires careful configuration, particularly in setting similarity thresholds to avoid serving incorrect responses due to false positives. Proper partitioning of caches by domain, use of domain-specific fine-tuned models, and the strategic selection of embedding models are crucial for optimizing the system's precision and recall while minimizing embedding computation costs. Redis offers a comprehensive solution for semantic caching by integrating vector search capabilities with its caching infrastructure, supporting various indexing algorithms and distance metrics, and providing tools for monitoring cache performance and ensuring accurate response delivery.

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