mccullocht opened a new pull request, #16685:
URL: https://github.com/apache/lucene/pull/16685

   Add a new Mode option to scalar quantization that allows switching between 3 
different storage representations:
   
   CENTERED maintains the existing path: a mean vector is computed per-segment 
and every vector is quantized as a residual against this mean vector. 
DATA_BLIND_WITH_FLOATS skips computing a mean vector and provides a zero vector 
instead. This makes merging cheaper -- there's no need to compute the segment 
mean vector or re-quantize the contents of the segment. The original input 
float vector is retained. DATA_BLIND_WITHOUT_FLOATS extends the approach above 
by dropping the float vectors as well. This only support for symmetric 
quantization; asymmetric quantization needs to re-read the original floats as 
"queries" for graph search. Both CENTERED and DATA_BLIND_WITH_FLOATS require 
that there is an input float field on segment inputs or merges are failed, when 
floats are dropped we allow inputs with he same encoding.


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