diff --git a/langchain/math_utils.py b/langchain/math_utils.py
index 218af047..f9e5e0dd 100644
--- a/langchain/math_utils.py
+++ b/langchain/math_utils.py
@@ -13,7 +13,10 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
     X = np.array(X)
     Y = np.array(Y)
     if X.shape[1] != Y.shape[1]:
-        raise ValueError("Number of columns in X and Y must be the same.")
+        raise ValueError(
+            f"Number of columns in X and Y must be the same. X has shape {X.shape} "
+            f"and Y has shape {Y.shape}."
+        )
 
     X_norm = np.linalg.norm(X, axis=1)
     Y_norm = np.linalg.norm(Y, axis=1)
diff --git a/langchain/vectorstores/utils.py b/langchain/vectorstores/utils.py
index 50e8ae6c..aead758b 100644
--- a/langchain/vectorstores/utils.py
+++ b/langchain/vectorstores/utils.py
@@ -16,7 +16,9 @@ def maximal_marginal_relevance(
     """Calculate maximal marginal relevance."""
     if min(k, len(embedding_list)) <= 0:
         return []
-    similarity_to_query = cosine_similarity([query_embedding], embedding_list)[0]
+    if query_embedding.ndim == 1:
+        query_embedding = np.expand_dims(query_embedding, axis=0)
+    similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0]
     most_similar = int(np.argmax(similarity_to_query))
     idxs = [most_similar]
     selected = np.array([embedding_list[most_similar]])
diff --git a/tests/unit_tests/vectorstores/__init__.py b/tests/unit_tests/vectorstores/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/unit_tests/vectorstores/test_utils.py b/tests/unit_tests/vectorstores/test_utils.py
new file mode 100644
index 00000000..6ad76e42
--- /dev/null
+++ b/tests/unit_tests/vectorstores/test_utils.py
@@ -0,0 +1,54 @@
+"""Test vector store utility functions."""
+import numpy as np
+
+from langchain.vectorstores.utils import maximal_marginal_relevance
+
+
+def test_maximal_marginal_relevance_lambda_zero() -> None:
+    query_embedding = np.random.random(size=5)
+    embedding_list = [query_embedding, query_embedding, np.zeros(5)]
+    expected = [0, 2]
+    actual = maximal_marginal_relevance(
+        query_embedding, embedding_list, lambda_mult=0, k=2
+    )
+    assert expected == actual
+
+
+def test_maximal_marginal_relevance_lambda_one() -> None:
+    query_embedding = np.random.random(size=5)
+    embedding_list = [query_embedding, query_embedding, np.zeros(5)]
+    expected = [0, 1]
+    actual = maximal_marginal_relevance(
+        query_embedding, embedding_list, lambda_mult=1, k=2
+    )
+    assert expected == actual
+
+
+def test_maximal_marginal_relevance() -> None:
+    query_embedding = np.array([1, 0])
+    # Vectors that are 30, 45 and 75 degrees from query vector (cosine similarity of
+    # 0.87, 0.71, 0.26) and the latter two are 15 and 60 degree from the first
+    # (cosine similarity 0.97 and 0.71). So for 3rd vector be chosen, must be case that
+    # 0.71lambda - 0.97(1 - lambda) < 0.26lambda - 0.71(1-lambda)
+    # -> lambda ~< .26 / .71
+    embedding_list = [[3**0.5, 1], [1, 1], [1, 2 + (3**0.5)]]
+    expected = [0, 2]
+    actual = maximal_marginal_relevance(
+        query_embedding, embedding_list, lambda_mult=(25 / 71), k=2
+    )
+    assert expected == actual
+
+    expected = [0, 1]
+    actual = maximal_marginal_relevance(
+        query_embedding, embedding_list, lambda_mult=(27 / 71), k=2
+    )
+    assert expected == actual
+
+
+def test_maximal_marginal_relevance_query_dim() -> None:
+    query_embedding = np.random.random(size=5)
+    query_embedding_2d = query_embedding.reshape((1, 5))
+    embedding_list = np.random.random(size=(4, 5)).tolist()
+    first = maximal_marginal_relevance(query_embedding, embedding_list)
+    second = maximal_marginal_relevance(query_embedding_2d, embedding_list)
+    assert first == second