fix(langchain): search() crashes with TypeError when score is None (#5072)

Co-authored-by: Kartik <kartik.labhshetwar@mem0.ai>

tests/vector_stores/test_langchain_vector_store.py

@@ -57,6 +57,9 @@ def test_search_vectors(langchain_instance):
     assert results[0].payload == {"name": "vector1"}
     assert results[1].id == "id2"
     assert results[1].payload == {"name": "vector2"}
+    # scores must never be None — score_and_rank crashes on None < threshold
+    assert results[0].score == 1.0
+    assert results[1].score == 1.0
 
     # Test search with filters
     filters = {"name": "vector1"}
@@ -226,6 +229,56 @@ def test_list_with_exception(langchain_instance):
     assert results == []
 
 
+def test_search_score_is_never_none(langchain_instance):
+    """Regression: similarity_search_by_vector returns Documents with no scores.
+    search() must not propagate None — score_and_rank crashes on None < threshold."""
+    from mem0.utils.scoring import score_and_rank
+
+    mock_docs = [Mock(metadata={"data": "mem A"}, id="id1"), Mock(metadata={"data": "mem B"}, id="id2")]
+    langchain_instance.client.similarity_search_by_vector.return_value = mock_docs
+
+    results = langchain_instance.search(query="test", vectors=[[0.1, 0.2]], top_k=5)
+
+    assert all(r.score is not None for r in results), "score must never be None"
+    # Fallback path: no scored method available, so 1.0 is assigned.
+    assert all(r.score == 1.0 for r in results)
+
+    # Verify the full pipeline does not raise TypeError
+    candidates = [{"id": r.id, "score": r.score, "payload": r.payload} for r in results]
+    ranked = score_and_rank(candidates, {}, {}, threshold=0.1, top_k=5)
+    assert len(ranked) == 2
+
+
+def test_search_uses_scored_method_when_available(langchain_instance):
+    """When a scored-by-vector method exists on the client, use it to get real scores."""
+    mock_docs = [Mock(metadata={"data": "mem A"}, id="id1"), Mock(metadata={"data": "mem B"}, id="id2")]
+    # Inject a non-spec method that returns (Document, float) pairs
+    langchain_instance.client.similarity_search_by_vector_with_relevance_scores = Mock(
+        return_value=[(mock_docs[0], 0.95), (mock_docs[1], 0.42)]
+    )
+
+    results = langchain_instance.search(query="test", vectors=[[0.1, 0.2]], top_k=5)
+
+    langchain_instance.client.similarity_search_by_vector_with_relevance_scores.assert_called_once()
+    langchain_instance.client.similarity_search_by_vector.assert_not_called()
+    assert results[0].score == pytest.approx(0.95)
+    assert results[1].score == pytest.approx(0.42)
+
+
+def test_search_falls_back_when_scored_method_raises_not_implemented(langchain_instance):
+    """If the scored method raises NotImplementedError, fall back to score=1.0."""
+    mock_docs = [Mock(metadata={"data": "mem A"}, id="id1")]
+    langchain_instance.client.similarity_search_by_vector_with_relevance_scores = Mock(
+        side_effect=NotImplementedError
+    )
+    langchain_instance.client.similarity_search_by_vector.return_value = mock_docs
+
+    results = langchain_instance.search(query="test", vectors=[[0.1, 0.2]], top_k=5)
+
+    langchain_instance.client.similarity_search_by_vector.assert_called_once()
+    assert results[0].score == 1.0
+
+
 def test_update_wraps_vector_and_payload_in_lists(langchain_instance):
     """Regression test for Langchain update() type mismatch.