RAG caching is a performance optimization technique that stores and reuses previously retrieved contexts in RAG systems. When a similar query appears, instead of performing a new retrieval operation, the system can use the cached results, significantly reducing latency and computational overhead.

Think of RAG caching like a smart librarian who remembers which books they’ve previously pulled for similar questions. There are three main strategies for implementing this caching system:

  • Query-based caching stores the exact retrieval results for specific queries. When the same query appears again, the system returns cached chunks without re-running retrieval. This is most effective for frequently repeated questions but doesn’t help with variations of the same question.
  • Semantic caching takes a more complicated approach by storing embeddings of previous queries and their results. When a new query is semantically similar to a cached one (high embedding similarity), the system can reuse those results. This helps catch variations of questions that mean the same thing.
  • Hybrid caching combines both approaches by using exact matches when available and falling back to semantic similarity when needed. This provides an optimal balance between precision and cache hit rates.

While implementing RAG caching can greatly improve system performance, especially for frequently asked questions, it’s crucial to implement proper cache invalidation strategies. Your knowledge base will evolve over time, and you need mechanisms to update or invalidate cached results when the underlying data changes. This ensures that users always receive accurate and up-to-date information.

You can implement this using various technologies, from simple in-memory caches for small systems to distributed caching solutions like Redis for large-scale applications. The choice depends on your specific needs for speed, scale, and consistency.

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Last Update: 23/01/2025