Search Results for "phase"

...The project targets workloads where embedding search needs to be compact, efficient, and practical to integrate into Python applications. It avoids a separate training phase for the quantizer, which can simplify setup compared with systems that require codebook learning. TurboVec is useful for developers building retrieval, ranking, semantic search, recommendation, or AI memory systems. Its main value is combining Rust performance with a Python-facing workflow for modern vector search experiments and applications.

Tracks your most used directories, based on 'frecency'. After a short learning phase, z will take you to the most 'frecent directory that matches ALL of the regexes given on the command line, in order. For example, z foo bar would match /foo/bar but not /bar/foo. The rank of directories maintained by z undergoes aging based on a simple formula. The rank of each entry is incremented every time it is accessed. When the sum of ranks is over 9000, all ranks are multiplied by 0.99. ...