Semantic Cache: Definition and Examples

The semantic cache is an optimization technique used in artificial intelligence applications that allows reusing previously generated responses from a language model (LLM) when a new query is semantically close to an already processed query. Unlike a traditional cache that requires an exact match between keys, the semantic cache uses vector embeddings to measure semantic proximity between two queries.

Its operation relies on a multi-step pipeline: when a user sends a prompt, it is first transformed into a vector (embedding) and then compared to vectors of cached queries through a similarity search (e.g., cosine similarity). If a sufficiently close vector is found above a defined confidence threshold, the associated response is returned directly without calling the LLM, significantly reducing latency and costs.

This approach is particularly useful in contexts where many users ask similar but differently phrased questions. For example, "How does GPT work?" and "Explain how GPT works" are two distinct phrasings but semantically equivalent. A traditional cache would treat them as two different queries, while a semantic cache will recognize their proximity.

Popular solutions include GPTCache (open source), Redis with the vector search module, or managed services offered by certain API platforms. The main challenge of the semantic cache lies in tuning the similarity threshold: too low, it will return unsuitable responses; too high, it will almost never serve from cache.