Imagine a scenario where a user poses a question to a chatbot, and instead of generating an answer from scratch every time, the system intelligently retrieves a precomputed response from a cache if a similar question has been asked before. This is the essence of semantic caching.
Semantic caching involves encoding questions into semantic vectors and building an index to efficiently retrieve responses. In this blog post, we'll explore the implementation of a SemanticCaching class using popular libraries such as Faiss for indexing and Sentence Transformers for semantic embeddings.