diff --git a/libs/langchain/langchain/utils/math.py b/libs/langchain/langchain/utils/math.py
index 77784ba2a49..99d47368197 100644
--- a/libs/langchain/langchain/utils/math.py
+++ b/libs/langchain/langchain/utils/math.py
@@ -1,8 +1,11 @@
 """Math utils."""
+import logging
 from typing import List, Optional, Tuple, Union
 
 import numpy as np
 
+logger = logging.getLogger(__name__)
+
 Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
 
 
@@ -10,6 +13,7 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
     """Row-wise cosine similarity between two equal-width matrices."""
     if len(X) == 0 or len(Y) == 0:
         return np.array([])
+
     X = np.array(X)
     Y = np.array(Y)
     if X.shape[1] != Y.shape[1]:
@@ -17,14 +21,27 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
             f"Number of columns in X and Y must be the same. X has shape {X.shape} "
             f"and Y has shape {Y.shape}."
         )
+    try:
+        import simsimd as simd
 
-    X_norm = np.linalg.norm(X, axis=1)
-    Y_norm = np.linalg.norm(Y, axis=1)
-    # Ignore divide by zero errors run time warnings as those are handled below.
-    with np.errstate(divide="ignore", invalid="ignore"):
-        similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm)
-    similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
-    return similarity
+        X = np.array(X, dtype=np.float32)
+        Y = np.array(Y, dtype=np.float32)
+        Z = 1 - simd.cdist(X, Y, metric="cosine")
+        if isinstance(Z, float):
+            return np.array([Z])
+        return Z
+    except ImportError:
+        logger.info(
+            "Unable to import simsimd, defaulting to NumPy implementation. If you want "
+            "to use simsimd please install with `pip install simsimd`."
+        )
+        X_norm = np.linalg.norm(X, axis=1)
+        Y_norm = np.linalg.norm(Y, axis=1)
+        # Ignore divide by zero errors run time warnings as those are handled below.
+        with np.errstate(divide="ignore", invalid="ignore"):
+            similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm)
+        similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
+        return similarity
 
 
 def cosine_similarity_top_k(
diff --git a/libs/langchain/tests/unit_tests/test_math_utils.py b/libs/langchain/tests/unit_tests/utils/test_math.py
similarity index 100%
rename from libs/langchain/tests/unit_tests/test_math_utils.py
rename to libs/langchain/tests/unit_tests/utils/test_math.py