jimczi commented on PR #13525:
URL: https://github.com/apache/lucene/pull/13525#issuecomment-2478490713

   > One reason to not add this would be if it makes the single vector setup 
hard to evolve. I'd like to understand if (and how) this is happening, and 
think on how we can address those concerns.
   
   I believe we should carefully consider the approach to adding multi-vector 
support through an aggregate function. From the outset, we assume that 
multi-vectors should be scored together, which is an important principle. 
Moreover, the default aggregate function proposed in the PR relies on brute 
force, which is not practical for any indexing setup.
   
   My concern is that this proposal doesn’t truly add support for independent 
multi-vectors. Instead, it introduces a block of vectors that must be scored 
together, which feels like a workaround rather than a comprehensive solution. 
This approach doesn’t address the key challenges of implementing true 
multi-vector support in the codec.
   
   The root issue is that the current KNN codec assumes the number of vectors 
is bounded by a single integer, a limitation that needs to be addressed first. 
Removing this constraint is a complex task but essential for properly 
supporting multi-vectors. Once that foundation is in place, adding support for 
setups like ColBERT should become relatively straightforward.
   
   Finally, while the max-sim function proposed in this PR may work as a 
ranking function, it isn’t suitable for indexing any documents. A true solution 
should allow for independent multi-vectors to be queried and scored flexibly 
without these constraints.


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