diff --git a/libs/community/tests/unit_tests/vectorstores/test_faiss.py b/libs/community/tests/unit_tests/vectorstores/test_faiss.py
index 8b6cf76751..db8228962c 100644
--- a/libs/community/tests/unit_tests/vectorstores/test_faiss.py
+++ b/libs/community/tests/unit_tests/vectorstores/test_faiss.py
@@ -10,6 +10,7 @@ from langchain_core.documents import Document
 from langchain_community.docstore.base import Docstore
 from langchain_community.docstore.in_memory import InMemoryDocstore
 from langchain_community.vectorstores.faiss import FAISS
+from langchain_community.vectorstores.utils import DistanceStrategy
 from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
 
 _PAGE_CONTENT = """This is a page about LangChain.
@@ -687,6 +688,26 @@ def test_missing_normalize_score_fn() -> None:
         faiss_instance.similarity_search_with_relevance_scores("foo", k=2)
 
 
+@pytest.mark.skip(reason="old relevance score feature")
+@pytest.mark.requires("faiss")
+def test_ip_score() -> None:
+    embedding = FakeEmbeddings()
+    vector = embedding.embed_query("hi")
+    assert vector == [1] * 9 + [0], f"FakeEmbeddings() has changed, produced {vector}"
+
+    db = FAISS.from_texts(
+        ["sundays coming so i drive my car"],
+        embedding=FakeEmbeddings(),
+        distance_strategy=DistanceStrategy.MAX_INNER_PRODUCT,
+    )
+    scores = db.similarity_search_with_relevance_scores("sundays", k=1)
+    assert len(scores) == 1, "only one vector should be in db"
+    _, score = scores[0]
+    assert (
+        score == 1
+    ), f"expected inner product of equivalent vectors to be 1, not {score}"
+
+
 @pytest.mark.requires("faiss")
 async def test_async_missing_normalize_score_fn() -> None:
     """Test doesn't perform similarity search without a valid distance strategy."""