What is semantic caching? AI inference optimization

Semantic caching is an optimization technique for AI model calls that stores and reuses the results of previous requests when new queries are semantically similar, even if their wording differs. Unlike traditional caching, which requires an exact key match, semantic caching uses embeddings and similarity thresholds to match queries by intent, akin to a librarian recognizing rephrased questions. It is particularly beneficial in scenarios involving large language models (LLMs) like GPT-4o, where inference processes are costly and slow compared to cached lookups. This method can significantly reduce costs, improve latency, and protect against rate limits, making it ideal for applications with repetitive queries such as customer support chatbots and data classification workflows. The process involves converting the incoming query into a vector embedding, comparing it against stored embeddings using tools like Pinecone or Redis, and applying a similarity threshold to decide whether to return a cached response or send the query to the LLM. Semantic caching is especially advantageous in automation workflows that frequently call LLMs, enhancing efficiency and reducing processing time in applications like CodeWords.

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