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based on https://github.com/lancedb/lancedb/pull/713 - The Reranker api can be plugged into vector only or fts only search but this PR doesn't do that (see example - https://txt.cohere.com/rerank/) ### Default reranker -- `LinearCombinationReranker(weight=0.7, fill=1.0)` ``` table.search("hello", query_type="hybrid").rerank(normalize="score").to_pandas() ``` ### Available rerankers LinearCombinationReranker ``` from lancedb.rerankers import LinearCombinationReranker # Same as default table.search("hello", query_type="hybrid").rerank( normalize="score", reranker=LinearCombinationReranker() ).to_pandas() # with custom params reranker = LinearCombinationReranker(weight=0.3, fill=1.0) table.search("hello", query_type="hybrid").rerank( normalize="score", reranker=reranker ).to_pandas() ``` Cohere Reranker ``` from lancedb.rerankers import CohereReranker # default model.. English and multi-lingual supported. See docstring for available custom params table.search("hello", query_type="hybrid").rerank( normalize="rank", # score or rank reranker=CohereReranker() ).to_pandas() ``` CrossEncoderReranker ``` from lancedb.rerankers import CrossEncoderReranker table.search("hello", query_type="hybrid").rerank( normalize="rank", reranker=CrossEncoderReranker() ).to_pandas() ``` ## Using custom Reranker ``` from lancedb.reranker import Reranker class CustomReranker(Reranker): def rerank_hybrid(self, vector_result, fts_result): combined_res = self.merge_results(vector_results, fts_results) # or use custom combination logic # Custom rerank logic here return combined_res ``` - [x] Expand testing - [x] Make sure usage makes sense - [x] Run simple benchmarks for correctness (Seeing weird result from cohere reranker in the toy example) - Support diverse rerankers by default: - [x] Cross encoding - [x] Cohere - [x] Reciprocal Rank Fusion --------- Co-authored-by: Chang She <759245+changhiskhan@users.noreply.github.com> Co-authored-by: Prashanth Rao <35005448+prrao87@users.noreply.github.com>