Boost Your RAG Systems with Semantic Caching

For retrieval-augmented generation (RAG) AI applications, semantic caching offers a powerful optimization to handle repetitive user queries efficiently. This technique involves storing embeddings of previously asked questions along with their answers in a high-speed cache.

How Semantic Caching Works

Instead of following the full RAG pipeline for every query, the system first checks the semantic cache. If a similar question is found based on embedding similarity, it retrieves the corresponding cached answer, bypassing the expensive vector database search and LLM generation steps.

Key Benefits

1. Reduced Computational Costs
2. Improved Response Times
3. Enhanced Scalability

Use Case Considerations

1. Most effective for factual/static question answering use cases
2. Requires careful cache management (size, eviction, refreshing)
3. Initial setup costs for cache infrastructure

Implementation

Popular options include FAISS for efficient similarity searches and key-value stores/databases supporting embedding storage. Integrate caching logic into your RAG pipeline, handling lookups, insertions, and updates. Monitor performance metrics like cache hit rates and response times.