Vector search indexes can take up far more space than the data they're searching. LEANN,…
This is a dev post classified by AGK Intelligence as Databases (a tool drop), kept by the Dev Radar because it carries real work, not commentary.
Vector search indexes can take up far more space than the data they're searching. LEANN, an open-source project from UC Berkeley’s Sky Computing Lab, aims to change that. Instead of storing every embedding, LEANN recomputes them when needed. In the project’s benchmark, a 60-million-chunk Wikipedia index went from 201 GB to just 6 GB. The trade-off is speed. LEANN achieved the same 25.5% downstream accuracy as HNSW in one benchmark, but retrieval took 2.48 seconds compared with just 0.05 seconds for HNSW. That’s a big reduction in storage, which could make running RAG and semantic search loc
Posted by Solomon Nnajieze (1.8k followers) 1 days ago · 0 likes · 16 views · view the original post on X. Kept by the Dev Radar as Databases.
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