Wire token-budget packing into the overview/rerank fallback path

apps/ai-service/tests/test_fusion.py

@@ -0,0 +1,57 @@
+import pytest
+
+from rag.fusion import reciprocal_rank_fusion
+from rag.models import RetrievalDocument, SearchHit, SourceRef
+
+
+SOURCE = SourceRef(physical_page=1, precision="page")
+
+
+def _hit(doc_id: str, score: float = 1.0) -> SearchHit:
+    return SearchHit(
+        RetrievalDocument(
+            doc_id=doc_id,
+            drug_id="drug",
+            kind="prose",
+            text=doc_id,
+            section_key="section",
+            source_refs=(SOURCE,),
+        ),
+        score,
+    )
+
+
+def test_rrf_promotes_candidate_supported_by_dense_and_lexical_rankings() -> None:
+    fused = reciprocal_rank_fusion(
+        [[_hit("dense-only"), _hit("shared")], [_hit("shared"), _hit("lexical-only")]],
+        rank_constant=60,
+    )
+    assert [hit.document.doc_id for hit in fused] == [
+        "shared", "dense-only", "lexical-only",
+    ]
+
+
+def test_rrf_deduplicates_a_document_within_one_ranking() -> None:
+    fused = reciprocal_rank_fusion(
+        [[_hit("duplicate"), _hit("duplicate")], [_hit("other")]],
+        rank_constant=10,
+    )
+    duplicate = next(hit for hit in fused if hit.document.doc_id == "duplicate")
+    assert duplicate.score == pytest.approx(1 / 11)
+
+
+def test_rrf_limit_and_ties_are_deterministic() -> None:
+    fused = reciprocal_rank_fusion(
+        [[_hit("first")], [_hit("second")]], rank_constant=60, limit=1,
+    )
+    assert [hit.document.doc_id for hit in fused] == ["first"]
+
+
+@pytest.mark.parametrize(
+    ("kwargs", "message"),
+    [({"rank_constant": 0}, "rank_constant"), ({"limit": -1}, "limit")],
+)
+def test_rrf_rejects_invalid_configuration(kwargs, message: str) -> None:
+    with pytest.raises(ValueError, match=message):
+        reciprocal_rank_fusion([], **kwargs)
+