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
Reduced Computational Costs
Improved Response Times
Enhanced Scalability
Use Case Considerations
Most effective for factual/static question answering use cases
Requires careful cache management (size, eviction, refreshing)
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.
While semantic caching has trade-offs, it presents a compelling optimization for RAG systems dealing with high volumes of repetitive queries. By intelligently caching responses, you can reduce costs, accelerate performance, and improve scalability for enhanced user experiences.
Curious to delve deeper into this?
Join Professor Mehdi as he delves into Semantic Cache, discussing the technique, its pros and cons for production, in the video below!👇
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Source of images/quotes:
🔨 Colab Implementation: https://colab.research.google.com/github/huggingface/cookbook/blob/main/notebooks/en/semantic_cache_chroma_vector_database.ipynb
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