UC Berkeley’s LEANN vector index stores a pruned graph and recomputes embeddings at query time. It matches HNSW accuracy at 90% recall using 4 GB instead of 188 GB, at the cost of seconds-level search.
The post Meet LEANN: An Open-Source Vector Index that Shrinks a 60M-Chunk RAG Index from 201 GB to 6 GB appeared first on MarkTechPost.
Google released EmbeddingGemma 2 on Tuesday, putting text, code, image, video, and audio retrieval into a 740-million-parameter open model that The post Your phone’s…
Code has become extremely cheap to produce, if you have a 5x claude subscription, you can produce or at least try to produce any piece of software you want. And, to be…
Article URL: https://www.tigerdata.com/blog/announcing-pg_vectorsearch-scalable-open-source-vector-search-postgresql Comments URL…
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