Optimizing LLM Performance with Semantic Caching

[oandreeva-nv]

Hello everyone,

I would like to open a discussion on Semantic Caching and its significance in deploying large language models (LLMs). As we strive for performance and cost-efficiency in LLM-based workflows, semantic caching offers a promising solution. By considering the semantics of requests rather than just raw data, it optimizes resource usage and enhances application scalability.

Our tutorial provides a reference implementation, which integrates key components like SentenceTransformer for embeddings, Faiss for similarity search, and Theine for caching. Please note that this implementation is limited and not officially supported, serving primarily as a conceptual guide to enhance efficiency and scalability while maintaining response consistency.

Could semantic caching be beneficial for your use case? What specific challenges might affect its adoption in your workflow? Your insights will help shape our efforts towards potential support of Semantic Caching in future releases.

Looking forward to your input!