AI & LLMs21 July 20268 min read

RAG Cache Strategies: Semantic Caching with Redis and Sentence Transformers

Avoid repeating $0.03 LLM queries by matching semantically similar user prompts in vector cache before hitting API endpoints.

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RAG Cache Strategies: Semantic Caching with Redis and Sentence Transformers Cover

Beyond Exact Key Caching

Exact string caching (e.g. MD5(prompt)) fails when users ask the same question with different wording. Semantic caching converts incoming prompts into vector embeddings and checks cosine similarity against historical cached queries.

References & Further Reading:

Redis Vector Search Documentation

#Python #LLMs #Prompt Engineering

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