How multi-vector search changes the reranking strategy

[YYGe01]

I want to take the maximum value of multiple vector similarity matches as the final score.
Currently I only see WeightedRanker and RRFRanker.
I hope I can customize my Ranker method.

[yhmo]

I suppose this is a new feature.
What kind of ranker do you want?

[YYGe01]

I suppose this is a new feature.我想这是一个新功能。 What kind of ranker do you want? 您想要什么样的排名？

like this.
https://discuss.elastic.co/t/how-to-modify-default-scoring-calculation-when-using-multiple-knn-mixed-searches/356513
For example, I store a piece of text in three text vectors, and I want to take the maximum value of the three vectors as the final score.

[yhmo]

The link of elasic shows a weight rank of vector search and text match.
Milvus only supports rank of vector search.

"take the maximum value of the three vectors" ---------- what is "maximum value of the thress vectors"?

[YYGe01]

Official documentation includes this example.
https://milvus.io/docs/multi-vector-search.md

request_1 = AnnSearchRequest(**search_param_1) 
request_2 = AnnSearchRequest(**search_param_2) 
reqs = [request_1, request_2] 
rerank = WeightedRanker(0.8, 0.2)  

The final score should be: 0.8 * request_1_score + 0.2 * request_2_score

I would like the rerank to be:

request_1 = AnnSearchRequest(**search_param_1) 
request_2 = AnnSearchRequest(**search_param_2) 
reqs = [request_1, request_2] 
rerank = MAX(reqs)

The final score is: max(request_1_score, request_2_score)

[yhmo]

Ok, I see. I think this is a new Ranker type that can be implemented.
Wait @xiaofan-luan comment.