use 1-indexed rank (instead of 0-based index)

Aleph-Alpha · Apr 8, 2024 · f909f9a · f909f9a
1 parent 033d8f1
commit f909f9a
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diff --git a/src/intelligence_layer/use_cases/search/search.py b/src/intelligence_layer/use_cases/search/search.py
@@ -107,9 +107,9 @@ def overlaps(a: tuple[int, int], b: tuple[int, int]) -> bool:
             b_start, b_end = b
             return a_start < b_end and b_start < a_end
 
-        index, score = next(
+        rank, score = next(
             (
-                (index, result.score)
+                (index + 1, result.score)
                 for index, result in enumerate(results)
                 if overlaps(
                     (result.document_chunk.start, result.document_chunk.end),
@@ -122,7 +122,7 @@ def overlaps(a: tuple[int, int], b: tuple[int, int]) -> bool:
             (None, None),
         )
 
-        return SearchEvaluation(rank=index, similarity_score=score)
+        return SearchEvaluation(rank=rank, similarity_score=score)
 
 
 class MeanTopK(BaseModel):

diff --git a/tests/use_cases/search/test_search.py b/tests/use_cases/search/test_search.py
@@ -74,7 +74,7 @@ def test_search(
 def test_search_evaluation_logic_works_for_overlapping_output(
     example: Example[SearchInput, ExpectedSearchOutput],
 ) -> None:
-    logic = SearchEvaluationLogic[SearchResult[str]]()
+    logic = SearchEvaluationLogic()
     output = SuccessfulExampleOutput(
         run_id="1",
         example_id="1",