fix: Normalize scores in RRF ranking in hybrid search

langchain_postgres/v2/async_vectorstore.py

@@ -670,6 +670,7 @@ async def __query_collection(
                 dense_results,
                 sparse_results,
                 **hybrid_search_config.fusion_function_parameters,
+                distance_strategy=self.distance_strategy,
             )
             return combined_results
         return dense_results

langchain_postgres/v2/hybrid_search_config.py

@@ -4,13 +4,16 @@
 
 from sqlalchemy import RowMapping
 
+from .indexes import DistanceStrategy
+
 
 def weighted_sum_ranking(
     primary_search_results: Sequence[RowMapping],
     secondary_search_results: Sequence[RowMapping],
     primary_results_weight: float = 0.5,
     secondary_results_weight: float = 0.5,
     fetch_top_k: int = 4,
+    **kwargs: Any,
 ) -> Sequence[dict[str, Any]]:
     """
     Ranks documents using a weighted sum of scores from two sources.
@@ -69,6 +72,7 @@ def reciprocal_rank_fusion(
     secondary_search_results: Sequence[RowMapping],
     rrf_k: float = 60,
     fetch_top_k: int = 4,
+    **kwargs: Any,
 ) -> Sequence[dict[str, Any]]:
     """
     Ranks documents using Reciprocal Rank Fusion (RRF) of scores from two sources.
@@ -87,35 +91,45 @@ def reciprocal_rank_fusion(
         A list of (document_id, rrf_score) tuples, sorted by rrf_score
         in descending order.
     """
+    distance_strategy = kwargs.get(
+        "distance_strategy", DistanceStrategy.COSINE_DISTANCE
+    )
     rrf_scores: dict[str, dict[str, Any]] = {}
 
     # Process results from primary source
-    for rank, row in enumerate(
-        sorted(primary_search_results, key=lambda item: item["distance"], reverse=True)
-    ):
-        values = list(row.values())
-        doc_id = str(values[0])
-        row_values = dict(row)
-        primary_score = rrf_scores[doc_id]["distance"] if doc_id in rrf_scores else 0.0
-        primary_score += 1.0 / (rank + rrf_k)
-        row_values["distance"] = primary_score
-        rrf_scores[doc_id] = row_values
+    # Determine sorting order based on the vector distance strategy.
+    # For COSINE & EUCLIDEAN(distance), we sort ascending (reverse=False).
+    # For INNER_PRODUCT (similarity), we sort descending (reverse=True).
+    is_similarity_metric = distance_strategy == DistanceStrategy.INNER_PRODUCT
+    sorted_primary = sorted(
+        primary_search_results,
+        key=lambda item: item["distance"],
+        reverse=is_similarity_metric,
+    )
+
+    for rank, row in enumerate(sorted_primary):
+        doc_id = str(list(row.values())[0])
+        if doc_id not in rrf_scores:
+            rrf_scores[doc_id] = dict(row)
+            rrf_scores[doc_id]["distance"] = 0.0
+        # Add the "normalized" rank score
+        rrf_scores[doc_id]["distance"] += 1.0 / (rank + rrf_k)
 
     # Process results from secondary source
-    for rank, row in enumerate(
-        sorted(
-            secondary_search_results, key=lambda item: item["distance"], reverse=True
-        )
-    ):
-        values = list(row.values())
-        doc_id = str(values[0])
-        row_values = dict(row)
-        secondary_score = (
-            rrf_scores[doc_id]["distance"] if doc_id in rrf_scores else 0.0
-        )
-        secondary_score += 1.0 / (rank + rrf_k)
-        row_values["distance"] = secondary_score
-        rrf_scores[doc_id] = row_values
+    # Keyword search relevance is always "higher is better" -> sort descending
+    sorted_secondary = sorted(
+        secondary_search_results,
+        key=lambda item: item["distance"],
+        reverse=True,
+    )
+
+    for rank, row in enumerate(sorted_secondary):
+        doc_id = str(list(row.values())[0])
+        if doc_id not in rrf_scores:
+            rrf_scores[doc_id] = dict(row)
+            rrf_scores[doc_id]["distance"] = 0.0
+        # Add the rank score from this list to the existing score
+        rrf_scores[doc_id]["distance"] += 1.0 / (rank + rrf_k)
 
     # Sort the results by rrf score in descending order
     # Sort the results by weighted score in descending order

tests/unit_tests/v2/test_hybrid_search_config.py

@@ -1,13 +1,17 @@
+from typing import cast
+
 import pytest
+from sqlalchemy import RowMapping
 
 from langchain_postgres.v2.hybrid_search_config import (
     reciprocal_rank_fusion,
     weighted_sum_ranking,
 )
+from langchain_postgres.v2.indexes import DistanceStrategy
 
 
 # Helper to create mock input items that mimic RowMapping for the fusion functions
-def get_row(doc_id: str, score: float, content: str = "content") -> dict:
+def get_row(doc_id: str, score: float, content: str = "content") -> RowMapping:
     """
     Simulates a RowMapping-like dictionary.
     The fusion functions expect to extract doc_id as the first value and
@@ -17,7 +21,8 @@ def get_row(doc_id: str, score: float, content: str = "content") -> dict:
     # Python dicts maintain insertion order (Python 3.7+).
     # This structure ensures list(row.values())[0] is doc_id and
     # list(row.values())[-1] is score.
-    return {"id_val": doc_id, "content_field": content, "distance": score}
+    row_dict = {"id_val": doc_id, "content_field": content, "distance": score}
+    return cast(RowMapping, row_dict)
 
 
 class TestWeightedSumRanking:
@@ -102,30 +107,31 @@ def test_fetch_top_k(self) -> None:
 
 class TestReciprocalRankFusion:
     def test_empty_inputs(self) -> None:
+        """Tests that the function handles empty inputs gracefully."""
         results = reciprocal_rank_fusion([], [])
         assert results == []
 
     def test_primary_only(self) -> None:
-        primary = [
-            get_row("p1", 0.8),
-            get_row("p2", 0.6),
-        ]  # p1 rank 0, p2 rank 1
+        """Tests RRF with only primary results using default cosine (lower is better)."""
+        primary = [get_row("p1", 0.8), get_row("p2", 0.6)]
         rrf_k = 60
-        # p1_score = 1 / (0 + 60)
-        # p2_score = 1 / (1 + 60)
+        # --- Calculation (Cosine: lower is better) ---
+        # Sorted order: p2 (0.6) -> rank 0; p1 (0.8) -> rank 1
+        # p2_score = 1 / (0 + 60)
+        # p1_score = 1 / (1 + 60)
         results = reciprocal_rank_fusion(primary, [], rrf_k=rrf_k)  # type: ignore
         assert len(results) == 2
-        assert results[0]["id_val"] == "p1"
+        assert results[0]["id_val"] == "p2"
         assert results[0]["distance"] == pytest.approx(1.0 / (0 + rrf_k))
-        assert results[1]["id_val"] == "p2"
+        assert results[1]["id_val"] == "p1"
         assert results[1]["distance"] == pytest.approx(1.0 / (1 + rrf_k))
 
     def test_secondary_only(self) -> None:
-        secondary = [
-            get_row("s1", 0.9),
-            get_row("s2", 0.7),
-        ]  # s1 rank 0, s2 rank 1
+        """Tests RRF with only secondary results (higher is better)."""
+        secondary = [get_row("s1", 0.9), get_row("s2", 0.7)]
         rrf_k = 60
+        # --- Calculation (Keyword: higher is better) ---
+        # Sorted order: s1 (0.9) -> rank 0; s2 (0.7) -> rank 1
         results = reciprocal_rank_fusion([], secondary, rrf_k=rrf_k)  # type: ignore
         assert len(results) == 2
         assert results[0]["id_val"] == "s1"
@@ -134,96 +140,130 @@ def test_secondary_only(self) -> None:
         assert results[1]["distance"] == pytest.approx(1.0 / (1 + rrf_k))
 
     def test_mixed_results_default_k(self) -> None:
-        primary = [get_row("common", 0.8), get_row("p_only", 0.7)]
-        secondary = [get_row("common", 0.9), get_row("s_only", 0.6)]
+        """Tests fusion with default cosine (lower better) and keyword (higher better)."""
+        primary = [
+            get_row("common", 0.8),
+            get_row("p_only", 0.7),
+        ]  # Order: p_only, common
+        secondary = [
+            get_row("common", 0.9),
+            get_row("s_only", 0.6),
+        ]  # Order: common, s_only
         rrf_k = 60
-        # common_score = (1/(0+k))_prim + (1/(0+k))_sec = 2/k
-        # p_only_score = (1/(1+k))_prim = 1/(k+1)
-        # s_only_score = (1/(1+k))_sec = 1/(k+1)
+        # --- Calculation ---
+        # common: rank 1 in P (1/61) + rank 0 in S (1/60) -> highest score
+        # p_only: rank 0 in P (1/60)
+        # s_only: rank 1 in S (1/61)
         results = reciprocal_rank_fusion(primary, secondary, rrf_k=rrf_k)  # type: ignore
         assert len(results) == 3
         assert results[0]["id_val"] == "common"
-        assert results[0]["distance"] == pytest.approx(2.0 / rrf_k)
-        # Check the next two elements, their order might vary due to tie in score
-        next_ids = {results[1]["id_val"], results[2]["id_val"]}
-        next_scores = {results[1]["distance"], results[2]["distance"]}
-        assert next_ids == {"p_only", "s_only"}
-        for score in next_scores:
-            assert score == pytest.approx(1.0 / (1 + rrf_k))
+        assert results[0]["distance"] == pytest.approx(1 / 61 + 1 / 60)
+        assert results[1]["id_val"] == "p_only"
+        assert results[1]["distance"] == pytest.approx(1 / 60)
+        assert results[2]["id_val"] == "s_only"
+        assert results[2]["distance"] == pytest.approx(1 / 61)
 
     def test_fetch_top_k_rrf(self) -> None:
+        """Tests that fetch_top_k limits results correctly after fusion."""
+        # Using cosine distance (lower is better)
         primary = [get_row(f"p{i}", (10 - i) / 10.0) for i in range(5)]
-        rrf_k = 1
-        results = reciprocal_rank_fusion(primary, [], rrf_k=rrf_k, fetch_top_k=2)  # type: ignore
+        # Scores: [1.0, 0.9, 0.8, 0.7, 0.6]
+        # Sorted order: p4 (0.6), p3 (0.7), p2 (0.8), ...
+        results = reciprocal_rank_fusion(primary, [], fetch_top_k=2)  # type: ignore
         assert len(results) == 2
-        assert results[0]["id_val"] == "p0"
-        assert results[0]["distance"] == pytest.approx(1.0 / (0 + rrf_k))
-        assert results[1]["id_val"] == "p1"
-        assert results[1]["distance"] == pytest.approx(1.0 / (1 + rrf_k))
+        assert results[0]["id_val"] == "p4"
+        assert results[1]["id_val"] == "p3"
 
     def test_rrf_content_preservation(self) -> None:
+        """Tests that the data from the first time a document is seen is kept."""
         primary = [get_row("doc1", 0.9, content="Primary Content")]
         secondary = [get_row("doc1", 0.8, content="Secondary Content")]
-        # RRF processes primary then secondary. If a doc is in both,
-        # the content from the secondary list will overwrite primary's.
-        results = reciprocal_rank_fusion(primary, secondary, rrf_k=60)  # type: ignore
+        # RRF processes primary first. When "doc1" is seen, its data is stored.
+        # It will not be overwritten by the "doc1" from the secondary list.
+        results = reciprocal_rank_fusion(primary, secondary)  # type: ignore
         assert len(results) == 1
         assert results[0]["id_val"] == "doc1"
-        assert results[0]["content_field"] == "Secondary Content"
+        assert results[0]["content_field"] == "Primary Content"
 
-        # If only in primary
-        results_prim_only = reciprocal_rank_fusion(primary, [], rrf_k=60)  # type: ignore
-        assert results_prim_only[0]["content_field"] == "Primary Content"
+        # If only in secondary
+        results_prim_only = reciprocal_rank_fusion([], secondary, rrf_k=60)  # type: ignore
+        assert results_prim_only[0]["content_field"] == "Secondary Content"
 
     def test_reordering_from_inputs_rrf(self) -> None:
-        """
-        Tests that RRF fused ranking can be different from both primary and secondary
-        input rankings.
-        Primary Order: A, B, C
-        Secondary Order: C, B, A
-        Fused Order: (A, C) tied, then B
-        """
-        primary = [
-            get_row("docA", 0.9),
-            get_row("docB", 0.8),
-            get_row("docC", 0.1),
-        ]
-        secondary = [
-            get_row("docC", 0.9),
-            get_row("docB", 0.5),
-            get_row("docA", 0.2),
-        ]
-        rrf_k = 1.0  # Using 1.0 for k to simplify rank score calculation
-        # docA_score = 1/(0+1) [P] + 1/(2+1) [S] = 1 + 1/3 = 4/3
-        # docB_score = 1/(1+1) [P] + 1/(1+1) [S] = 1/2 + 1/2 = 1
-        # docC_score = 1/(2+1) [P] + 1/(0+1) [S] = 1/3 + 1 = 4/3
+        """Tests that RRF can produce a ranking different from the inputs."""
+        primary = [get_row("docA", 0.9), get_row("docB", 0.8), get_row("docC", 0.1)]
+        secondary = [get_row("docC", 0.9), get_row("docB", 0.5), get_row("docA", 0.2)]
+        rrf_k = 1.0
+        # --- Calculation (Primary sorted ascending, Secondary descending) ---
+        # Primary ranks: docC (0), docB (1), docA (2)
+        # Secondary ranks: docC (0), docB (1), docA (2)
+        # docC_score = 1/(0+1) [P] + 1/(0+1) [S] = 2.0
+        # docB_score = 1/(1+1) [P] + 1/(1+1) [S] = 1.0
+        # docA_score = 1/(2+1) [P] + 1/(2+1) [S] = 2/3
         results = reciprocal_rank_fusion(primary, secondary, rrf_k=rrf_k)  # type: ignore
         assert len(results) == 3
-        assert {results[0]["id_val"], results[1]["id_val"]} == {"docA", "docC"}
-        assert results[0]["distance"] == pytest.approx(4.0 / 3.0)
-        assert results[1]["distance"] == pytest.approx(4.0 / 3.0)
-        assert results[2]["id_val"] == "docB"
-        assert results[2]["distance"] == pytest.approx(1.0)
-
-    def test_reordering_from_inputs_weighted_sum(self) -> None:
-        """
-        Tests that the fused ranking can be different from both primary and secondary
-        input rankings.
-        Primary Order: A (0.9), B (0.7)
-        Secondary Order: B (0.8), A (0.2)
-        Fusion (0.5/0.5 weights):
-        docA_score = (0.9 * 0.5) + (0.2 * 0.5) = 0.45 + 0.10 = 0.55
-        docB_score = (0.7 * 0.5) + (0.8 * 0.5) = 0.35 + 0.40 = 0.75
-        Expected Fused Order: docB (0.75), docA (0.55)
-        This is different from Primary (A,B) and Secondary (B,A) in terms of
-        original score, but the fusion logic changes the effective contribution).
-        """
-        primary = [get_row("docA", 0.9), get_row("docB", 0.7)]
-        secondary = [get_row("docB", 0.8), get_row("docA", 0.2)]
+        assert results[0]["id_val"] == "docC"
+        assert results[0]["distance"] == pytest.approx(2.0)
+        assert results[1]["id_val"] == "docB"
+        assert results[1]["distance"] == pytest.approx(1.0)
+        assert results[2]["id_val"] == "docA"
+        assert results[2]["distance"] == pytest.approx(2.0 / 3.0)
 
-        results = weighted_sum_ranking(primary, secondary)  # type: ignore
+    # --------------------------------------------------------------------------
+    ## New Tests for Other Strategies and Edge Cases
+
+    def test_mixed_results_max_inner_product(self) -> None:
+        """Tests fusion with MAX_INNER_PRODUCT (higher is better) for primary."""
+        primary = [get_row("best", 0.9), get_row("worst", 0.1)]  # Order: best, worst
+        secondary = [get_row("best", 20.0), get_row("worst", 5.0)]  # Order: best, worst
+        rrf_k = 10
+        # best: rank 0 in P + rank 0 in S -> 1/10 + 1/10 = 0.2
+        # worst: rank 1 in P + rank 1 in S -> 1/11 + 1/11
+        results = reciprocal_rank_fusion(
+            primary,  # type: ignore
+            secondary,  # type: ignore
+            rrf_k=rrf_k,
+            distance_strategy=DistanceStrategy.INNER_PRODUCT,
+        )
+        assert len(results) == 2
+        assert results[0]["id_val"] == "best"
+        assert results[0]["distance"] == pytest.approx(0.2)
+        assert results[1]["id_val"] == "worst"
+        assert results[1]["distance"] == pytest.approx(2.0 / 11.0)
+
+    def test_mixed_results_euclidean(self) -> None:
+        """Tests fusion with EUCLIDEAN (lower is better) for primary."""
+        primary = [
+            get_row("closer", 10.5),
+            get_row("farther", 25.5),
+        ]  # Order: closer, farther
+        secondary = [
+            get_row("closer", 100.0),
+            get_row("farther", 10.0),
+        ]  # Order: closer, farther
+        rrf_k = 10
+        # closer: rank 0 in P + rank 0 in S -> 1/10 + 1/10 = 0.2
+        # farther: rank 1 in P + rank 1 in S -> 1/11 + 1/11
+        results = reciprocal_rank_fusion(
+            primary,  # type: ignore
+            secondary,  # type: ignore
+            rrf_k=rrf_k,
+            distance_strategy=DistanceStrategy.EUCLIDEAN,
+        )
         assert len(results) == 2
-        assert results[0]["id_val"] == "docB"
-        assert results[0]["distance"] == pytest.approx(0.75)
-        assert results[1]["id_val"] == "docA"
-        assert results[1]["distance"] == pytest.approx(0.55)
+        assert results[0]["id_val"] == "closer"
+        assert results[0]["distance"] == pytest.approx(0.2)
+        assert results[1]["id_val"] == "farther"
+        assert results[1]["distance"] == pytest.approx(2.0 / 11.0)
+
+    def test_rrf_with_identical_scores(self) -> None:
+        """Tests that stable sort is preserved for identical scores."""
+        # Python's sorted() is stable. p1 appears before p2 in the list.
+        primary = [get_row("p1", 0.5), get_row("p2", 0.5)]
+        rrf_k = 60
+        # Expected order (stable sort): p1 (rank 0), p2 (rank 1)
+        results = reciprocal_rank_fusion(primary, [])  # type: ignore
+        assert results[0]["id_val"] == "p1"
+        assert results[0]["distance"] == pytest.approx(1 / 60)
+        assert results[1]["id_val"] == "p2"
+        assert results[1]["distance"] == pytest.approx(1 / 61)