A key step in optimizing the response speed of RAG systems based on LMCache:
- Document pre-caching: Pre-cache key-value pairs of commonly queried documents to disk or Redis
- Enable non-prefix reuse: Exploit LMCache's support for non-prefixed text reuse to handle similar but differently ordered queries
- distributed deployment: Use multi-node caching to speed up indexing when the document volume is high
- test and verify: Use
lmcache-testsWarehouse workload generator for performance testing
This method is especially suitable for scenarios such as enterprise knowledge base, which is measured to reduce 30-50% of duplicate computation time. It is recommended to combine with vLLM's chunking function to achieve the best results.
This answer comes from the articleLMCache: A Key-Value Cache Optimization Tool for Accelerating Reasoning on Large Language ModelsThe
May not be reproduced without permission:AI productivity tools " How to improve the responsiveness of Retrieval Augmented Generation (RAG) systems?
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