feat(python): multi-vector reranking support (#1481)

Currently targeting the following usage:
```
from lancedb.rerankers import CrossEncoderReranker

reranker = CrossEncoderReranker()

query = "hello"

res1 = table.search(query, vector_column_name="vector").limit(3)
res2 = table.search(query, vector_column_name="text_vector").limit(3)
res3 = table.search(query, vector_column_name="meta_vector").limit(3)

reranked = reranker.rerank_multivector(
               [res1, res2, res3],  
              deduplicate=True,
              query=query # some reranker models need query
)
```
- This implements rerank_multivector function in the base reranker so
that all rerankers that implement rerank_vector will automatically have
multivector reranking support
- Special case for RRF reranker that just uses its existing
rerank_hybrid fcn to multi-vector reranking.

---------

Co-authored-by: Weston Pace <weston.pace@gmail.com>

python/python/lancedb/rerankers/cohere.py

@@ -88,7 +88,7 @@ class CohereReranker(Reranker):
         combined_results = self.merge_results(vector_results, fts_results)
         combined_results = self._rerank(combined_results, query)
         if self.score == "relevance":
-            combined_results = combined_results.drop_columns(["score", "_distance"])
+            combined_results = self._keep_relevance_score(combined_results)
         elif self.score == "all":
             raise NotImplementedError(
                 "return_score='all' not implemented for cohere reranker"