I built a vector embedding cache that makes stale hits structurally impossible
A new vector embedding cache called embcache has been developed to prevent stale hits in machine learning models. This cache utilizes a composite EmbeddingFingerprint to ensure that outdated vectors are not returned after updates. The author is seeking feedback on the fingerprint schema and has shared performance benchmarks indicating significant speed improvements.
- ▪The embcache is a GPU-native two-tier cache designed for embeddings and KV states.
- ▪It addresses the issue of stale vectors being returned after model upgrades or tokenizer changes.
- ▪The cache achieves a hit rate of 98.3% and offers a speedup of 400-450 times on KV cache hits.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3906874) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } BN Posted on May 16 I built a vector embedding cache that makes stale hits structurally impossible #rag #llm #python #vectordatabase Wrote up the design behind embcache, a GPU-native two-tier cache for embeddings and KV states. The problem it solves: embedding caches that key on content hash alone silently return stale vectors after a model upgrade or tokenizer change. The cache looks healthy. The vectors are wrong.
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