Smarter RAG, Smaller Bill: Optimize for Performance and Price
summary
RAGCache caches LLM key‑value tensors in GPU and host memory using a dynamic knowledge tree and a prefix-aware dual-size-frequency replacement policy (PGDSF), serving semantically matched embeddings to skip retrieval. Integrated with vLLM, Faiss and GPT Cache (works with LangChain), it claims up to 4x faster time‑to‑first‑token and roughly 2.1x throughput improvements versus vanilla RAG setups, cutting repeated recomputation and API calls to lower latency and cost.
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chapters (11)
- 0:00Smarter RAG optimization
- 1:03Traditional RAG limitations
- 2:08RAG caching benefits
- 3:55RAG cache infrastructure
- 5:20Benchmarks: Mistral vs Llama
- 6:44RAG cache vs FAQ systems
- 7:46GPT Cache library
- 9:14Real-world RAG caching
- 10:17Summary: RAG caching benefits
- 10:39Q&A: Text retrieval & GPU storage
- 12:48Q&A: Security & cache poisoning
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