Why JVector 3 Is The Most Advanced Embedded Vector Search Engine скачать в хорошем качестве

Why JVector 3 Is The Most Advanced Embedded Vector Search Engine

An airhacks.fm (https://airhacks.fm) conversation with Jonathan Ellis (@spyced (  / spyced  ) ) about: discussion of JVector (https://github.com/jbellis/jvector) 3 features and improvements, compression techniques for vector indexes, binary quantization vs product quantization, anisotropic (https://en.wikipedia.org/wiki/Anisotropy) product quantization for improved accuracy, indexing Wikipedia example, Cassandra (https://cassandra.apache.org/_/index....) integration, SIMD (https://en.wikipedia.org/wiki/Single_...) acceleration with Fused ADC (https://github.com/jbellis/jvector) , optimization with Chronicle Map (https://github.com/OpenHFT/Chronicle-Map) , E5 embedding models (https://huggingface.co/intfloat/e5-sm...) , comparison of CPU vs GPU for vector search, implementation details and low-level optimizations in Java (https://www.java.com/en/) , use of Java Panama (https://openjdk.java.net/projects/pan...) API and foreign function interface, JVector's performance advantages, memory footprint reduction, integration with Cassandra and Astra DB (https://www.datastax.com/products/dat...) , challenges of vector search at scale, trade-offs between RAM usage and CPU performance, Eventual Consistency (https://en.wikipedia.org/wiki/Eventua...) in distributed vector search, comparison of different embedding models and their accuracy, importance of re-ranking in vector search, advantages of JVector over other vector search implementations Jonathan Ellis on twitter: @spyced (  / spyced  )