How would you get around this problem - you have 2 sets of results from 2 different search engines - for example, one is vector and the other one is full-text. However, it just so happens that the vector search results are super good but the full-text search results are really crappy for this particular query (Not always). Now you apply the Reciprocial Rank Fusion algorithm and it blends together crap and quality instead of keeping more quality and discarding more crap. Wish there was a way to address this problem other than Elastic's custom "script_score" which is basically a static function and assumes that the same scoring algorithm will be applied regardless of input (results)
How would you get around this problem - you have 2 sets of results from 2 different search engines - for example, one is vector and the other one is full-text. However, it just so happens that the vector search results are super good but the full-text search results are really crappy for this particular query (Not always). Now you apply the Reciprocial Rank Fusion algorithm and it blends together crap and quality instead of keeping more quality and discarding more crap. Wish there was a way to address this problem other than Elastic's custom "script_score" which is basically a static function and assumes that the same scoring algorithm will be applied regardless of input (results)
Better to use a Reranker on top of it
build a LLM reranker