How to combine default BM25 score of Elasticsearch and Dense Vectors similarity

i found a issue about how to combine the both default bm25 and vectors, but i try it and found the result was poor, is there any official document about how to combine default BM25 score of Elasticsearch and Dense Vectors similarity ? including how to set the important weight. Maybe more examples will help us to understand this powerful tools.

There is no any official recipe how to set up these weights. You can frame this problem as as ML parameter optimization problem for your particular dataset.

I can refer you to our blog how this parameter optimization problem can be approached: Improving search relevance with data-driven query optimization

awesome! thank you very much