feat: Added Hybrid Search Config and Tests [1/N]

vishwarajanand · vishwarajanand · commit 22088a1e0902 · 2025-05-19T16:39:02.000Z
diff --git a/langchain_postgres/v2/hybrid_search_config.py b/langchain_postgres/v2/hybrid_search_config.py
@@ -0,0 +1,143 @@
+from abc import ABC
+from dataclasses import dataclass, field
+from typing import Any, Callable, Optional, Sequence
+
+from sqlalchemy import RowMapping
+
+
+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,
+) -> Sequence[dict[str, Any]]:
+    """
+    Ranks documents using a weighted sum of scores from two sources.
+
+    Args:
+        primary_search_results: A list of (document, distance) tuples from
+            the primary search.
+        secondary_search_results: A list of (document, distance) tuples from
+            the secondary search.
+        primary_results_weight: The weight for the primary source's scores.
+            Defaults to 0.5.
+        secondary_results_weight: The weight for the secondary source's scores.
+            Defaults to 0.5.
+        fetch_top_k: The number of documents to fetch after merging the results.
+            Defaults to 4.
+
+    Returns:
+        A list of (document, distance) tuples, sorted by weighted_score in
+        descending order.
+    """
+
+    # stores computed metric with provided distance metric and weights
+    weighted_scores: dict[str, dict[str, Any]] = {}
+
+    # Process results from primary source
+    for row in primary_search_results:
+        values = list(row.values())
+        doc_id = str(values[0])  # first value is doc_id
+        distance = float(values[-1])  # type: ignore # last value is distance
+        row_values = dict(row)
+        row_values["distance"] = primary_results_weight * distance
+        weighted_scores[doc_id] = row_values
+
+    # Process results from secondary source,
+    # adding to existing scores or creating new ones
+    for row in secondary_search_results:
+        values = list(row.values())
+        doc_id = str(values[0])  # first value is doc_id
+        distance = float(values[-1])  # type: ignore # last value is distance
+        primary_score = (
+            weighted_scores[doc_id]["distance"] if doc_id in weighted_scores else 0.0
+        )
+        row_values = dict(row)
+        row_values["distance"] = distance * secondary_results_weight + primary_score
+        weighted_scores[doc_id] = row_values
+
+    # Sort the results by weighted score in descending order
+    ranked_results = sorted(
+        weighted_scores.values(), key=lambda item: item["distance"], reverse=True
+    )
+    return ranked_results[:fetch_top_k]
+
+
+def reciprocal_rank_fusion(
+    primary_search_results: Sequence[RowMapping],
+    secondary_search_results: Sequence[RowMapping],
+    rrf_k: float = 60,
+    fetch_top_k: int = 4,
+) -> Sequence[dict[str, Any]]:
+    """
+    Ranks documents using Reciprocal Rank Fusion (RRF) of scores from two sources.
+
+    Args:
+        primary_search_results: A list of (document, distance) tuples from
+            the primary search.
+        secondary_search_results: A list of (document, distance) tuples from
+            the secondary search.
+        rrf_k: The RRF parameter k.
+            Defaults to 60.
+        fetch_top_k: The number of documents to fetch after merging the results.
+            Defaults to 4.
+
+    Returns:
+        A list of (document_id, rrf_score) tuples, sorted by rrf_score
+        in descending order.
+    """
+    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
+
+    # 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
+
+    # Sort the results by rrf score in descending order
+    # Sort the results by weighted score in descending order
+    ranked_results = sorted(
+        rrf_scores.values(), key=lambda item: item["distance"], reverse=True
+    )
+    # Extract only the RowMapping for the top results
+    return ranked_results[:fetch_top_k]
+
+
+@dataclass
+class HybridSearchConfig(ABC):
+    """Google AlloyDB Vector Store Hybrid Search Config."""
+
+    tsv_column: Optional[str] = ""
+    tsv_lang: Optional[str] = "pg_catalog.english"
+    fts_query: Optional[str] = ""
+    fusion_function: Callable[
+        [Sequence[RowMapping], Sequence[RowMapping], Any], Sequence[Any]
+    ] = weighted_sum_ranking  # Updated default
+    fusion_function_parameters: dict[str, Any] = field(default_factory=dict)
+    primary_top_k: int = 4
+    secondary_top_k: int = 4
+    index_name: str = "langchain_tsv_index"
+    index_type: str = "GIN"
diff --git a/tests/unit_tests/v2/test_hybrid_search_config.py b/tests/unit_tests/v2/test_hybrid_search_config.py
@@ -0,0 +1,220 @@
+import pytest
+
+from langchain_postgres.v2.hybrid_search_config import (reciprocal_rank_fusion,
+                                                        weighted_sum_ranking)
+
+
+# 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:
+    """
+    Simulates a RowMapping-like dictionary.
+    The fusion functions expect to extract doc_id as the first value and
+    the initial score/distance as the last value when casting values from RowMapping.
+    They then operate on dictionaries, using the 'distance' key for the fused score.
+    """
+    # 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}
+
+
+class TestWeightedSumRanking:
+    def test_empty_inputs(self):
+        results = weighted_sum_ranking([], [])
+        assert results == []
+
+    def test_primary_only(self):
+        primary = [get_row("p1", 0.8), get_row("p2", 0.6)]
+        # Expected scores: p1 = 0.8 * 0.5 = 0.4, p2 = 0.6 * 0.5 = 0.3
+        results = weighted_sum_ranking(
+            primary, [], primary_results_weight=0.5, secondary_results_weight=0.5
+        )
+        assert len(results) == 2
+        assert results[0]["id_val"] == "p1"
+        assert results[0]["distance"] == pytest.approx(0.4)
+        assert results[1]["id_val"] == "p2"
+        assert results[1]["distance"] == pytest.approx(0.3)
+
+    def test_secondary_only(self):
+        secondary = [get_row("s1", 0.9), get_row("s2", 0.7)]
+        # Expected scores: s1 = 0.9 * 0.5 = 0.45, s2 = 0.7 * 0.5 = 0.35
+        results = weighted_sum_ranking(
+            [], secondary, primary_results_weight=0.5, secondary_results_weight=0.5
+        )
+        assert len(results) == 2
+        assert results[0]["id_val"] == "s1"
+        assert results[0]["distance"] == pytest.approx(0.45)
+        assert results[1]["id_val"] == "s2"
+        assert results[1]["distance"] == pytest.approx(0.35)
+
+    def test_mixed_results_default_weights(self):
+        primary = [get_row("common", 0.8), get_row("p_only", 0.7)]
+        secondary = [get_row("common", 0.9), get_row("s_only", 0.6)]
+        # Weights are 0.5, 0.5
+        # common_score = (0.8 * 0.5) + (0.9 * 0.5) = 0.4 + 0.45 = 0.85
+        # p_only_score = (0.7 * 0.5) = 0.35
+        # s_only_score = (0.6 * 0.5) = 0.30
+        # Order: common (0.85), p_only (0.35), s_only (0.30)
+
+        results = weighted_sum_ranking(primary, secondary)
+        assert len(results) == 3
+        assert results[0]["id_val"] == "common"
+        assert results[0]["distance"] == pytest.approx(0.85)
+        assert results[1]["id_val"] == "p_only"
+        assert results[1]["distance"] == pytest.approx(0.35)
+        assert results[2]["id_val"] == "s_only"
+        assert results[2]["distance"] == pytest.approx(0.30)
+
+    def test_mixed_results_custom_weights(self):
+        primary = [get_row("d1", 1.0)]  # p_w=0.2 -> 0.2
+        secondary = [get_row("d1", 0.5)]  # s_w=0.8 -> 0.4
+        # Expected: d1_score = (1.0 * 0.2) + (0.5 * 0.8) = 0.2 + 0.4 = 0.6
+
+        results = weighted_sum_ranking(
+            primary, secondary, primary_results_weight=0.2, secondary_results_weight=0.8
+        )
+        assert len(results) == 1
+        assert results[0]["id_val"] == "d1"
+        assert results[0]["distance"] == pytest.approx(0.6)
+
+    def test_fetch_top_k(self):
+        primary = [get_row(f"p{i}", (10 - i) / 10.0) for i in range(5)]
+        # Scores: 1.0, 0.9, 0.8, 0.7, 0.6
+        # Weighted (0.5): 0.5, 0.45, 0.4, 0.35, 0.3
+        secondary = []
+        results = weighted_sum_ranking(primary, secondary, fetch_top_k=2)
+        assert len(results) == 2
+        assert results[0]["id_val"] == "p0"
+        assert results[0]["distance"] == pytest.approx(0.5)
+        assert results[1]["id_val"] == "p1"
+        assert results[1]["distance"] == pytest.approx(0.45)
+
+
+class TestReciprocalRankFusion:
+    def test_empty_inputs(self):
+        results = reciprocal_rank_fusion([], [])
+        assert results == []
+
+    def test_primary_only(self):
+        primary = [
+            get_row("p1", 0.8),
+            get_row("p2", 0.6),
+        ]  # p1 rank 0, p2 rank 1
+        rrf_k = 60
+        # p1_score = 1 / (0 + 60)
+        # p2_score = 1 / (1 + 60)
+        results = reciprocal_rank_fusion(primary, [], rrf_k=rrf_k)
+        assert len(results) == 2
+        assert results[0]["id_val"] == "p1"
+        assert results[0]["distance"] == pytest.approx(1.0 / (0 + rrf_k))
+        assert results[1]["id_val"] == "p2"
+        assert results[1]["distance"] == pytest.approx(1.0 / (1 + rrf_k))
+
+    def test_secondary_only(self):
+        secondary = [
+            get_row("s1", 0.9),
+            get_row("s2", 0.7),
+        ]  # s1 rank 0, s2 rank 1
+        rrf_k = 60
+        results = reciprocal_rank_fusion([], secondary, rrf_k=rrf_k)
+        assert len(results) == 2
+        assert results[0]["id_val"] == "s1"
+        assert results[0]["distance"] == pytest.approx(1.0 / (0 + rrf_k))
+        assert results[1]["id_val"] == "s2"
+        assert results[1]["distance"] == pytest.approx(1.0 / (1 + rrf_k))
+
+    def test_mixed_results_default_k(self):
+        primary = [get_row("common", 0.8), get_row("p_only", 0.7)]
+        secondary = [get_row("common", 0.9), get_row("s_only", 0.6)]
+        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)
+        results = reciprocal_rank_fusion(primary, secondary, rrf_k=rrf_k)
+        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))
+
+    def test_fetch_top_k_rrf(self):
+        primary = [get_row(f"p{i}", (10 - i) / 10.0) for i in range(5)]
+        secondary = []
+        rrf_k = 1
+        results = reciprocal_rank_fusion(primary, secondary, rrf_k=rrf_k, fetch_top_k=2)
+        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))
+
+    def test_rrf_content_preservation(self):
+        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)
+        assert len(results) == 1
+        assert results[0]["id_val"] == "doc1"
+        assert results[0]["content_field"] == "Secondary Content"
+
+        # If only in primary
+        results_prim_only = reciprocal_rank_fusion(primary, [], rrf_k=60)
+        assert results_prim_only[0]["content_field"] == "Primary Content"
+
+    def test_reordering_from_inputs_rrf(self):
+        """
+        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
+        results = reciprocal_rank_fusion(primary, secondary, rrf_k=rrf_k)
+        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):
+        """
+        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)]
+
+        results = weighted_sum_ranking(primary, secondary)
+        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)
