feat!: enforce all rerankers always return relevance score & deprecate linear combination fixes (#1687)

- Enforce all rerankers always return _relevance_score. This was already
loosely done in tests before but based on user feedback its better to
always have _relevance_score present in all reranked results
- Deprecate LinearCombinationReranker in docs. And also fix a case where
it would not return _relevance_score if one result set was missing

python/python/tests/test_rerankers.py

@@ -120,12 +120,14 @@ def _run_test_reranker(reranker, table, query, query_vector, schema):
     )
 
     assert len(result) == 30
-    err = (
+    ascending_relevance_err = (
         "The _relevance_score column of the results returned by the reranker "
         "represents the relevance of the result to the query & should "
         "be descending."
     )
-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+    assert np.all(
+        np.diff(result.column("_relevance_score").to_numpy()) <= 0
+    ), ascending_relevance_err
 
     # Vector search setting
     result = (
@@ -135,7 +137,9 @@ def _run_test_reranker(reranker, table, query, query_vector, schema):
         .to_arrow()
     )
     assert len(result) == 30
-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+    assert np.all(
+        np.diff(result.column("_relevance_score").to_numpy()) <= 0
+    ), ascending_relevance_err
     result_explicit = (
         table.search(query_vector, vector_column_name="vector")
         .rerank(reranker=reranker, query_string=query)
@@ -158,7 +162,26 @@ def _run_test_reranker(reranker, table, query, query_vector, schema):
         .to_arrow()
     )
     assert len(result) > 0
-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+    assert np.all(
+        np.diff(result.column("_relevance_score").to_numpy()) <= 0
+    ), ascending_relevance_err
+
+    # empty FTS results
+    query = "abcxyz" * 100
+    result = (
+        table.search(query_type="hybrid", vector_column_name="vector")
+        .vector(query_vector)
+        .text(query)
+        .limit(30)
+        .rerank(reranker=reranker)
+        .to_arrow()
+    )
+
+    # should return _relevance_score column
+    assert "_relevance_score" in result.column_names
+    assert np.all(
+        np.diff(result.column("_relevance_score").to_numpy()) <= 0
+    ), ascending_relevance_err
 
     # Multi-vector search setting
     rs1 = table.search(query, vector_column_name="vector").limit(10).with_row_id(True)
@@ -172,7 +195,7 @@ def _run_test_reranker(reranker, table, query, query_vector, schema):
     result_deduped = reranker.rerank_multivector(
         [rs1, rs2, rs1], query, deduplicate=True
     )
-    assert len(result_deduped) < 20
+    assert len(result_deduped) <= 20
     result_arrow = reranker.rerank_multivector([rs1.to_arrow(), rs2.to_arrow()], query)
     assert len(result) == 20 and result == result_arrow
 
@@ -213,7 +236,7 @@ def _run_test_hybrid_reranker(reranker, tmp_path, use_tantivy):
         .vector(query_vector)
         .text(query)
         .limit(30)
-        .rerank(normalize="score")
+        .rerank(reranker, normalize="score")
         .to_arrow()
     )
     assert len(result) == 30
@@ -228,12 +251,30 @@ def _run_test_hybrid_reranker(reranker, tmp_path, use_tantivy):
         table.search(query, query_type="hybrid", vector_column_name="vector").text(
             query
         ).to_arrow()
-
-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), (
+    ascending_relevance_err = (
         "The _relevance_score column of the results returned by the reranker "
         "represents the relevance of the result to the query & should "
         "be descending."
     )
+    assert np.all(
+        np.diff(result.column("_relevance_score").to_numpy()) <= 0
+    ), ascending_relevance_err
+
+    # Test with empty FTS results
+    query = "abcxyz" * 100
+    result = (
+        table.search(query_type="hybrid", vector_column_name="vector")
+        .vector(query_vector)
+        .text(query)
+        .limit(30)
+        .rerank(reranker=reranker)
+        .to_arrow()
+    )
+    # should return _relevance_score column
+    assert "_relevance_score" in result.column_names
+    assert np.all(
+        np.diff(result.column("_relevance_score").to_numpy()) <= 0
+    ), ascending_relevance_err
 
 
 @pytest.mark.parametrize("use_tantivy", [True, False])