From f3080b8a11cfe9d25411b1e9de6a42759f7a4cc0 Mon Sep 17 00:00:00 2001
From: "codeflash-ai[bot]"
 <148906541+codeflash-ai[bot]@users.noreply.github.com>
Date: Fri, 2 Feb 2024 04:55:18 +0000
Subject: [PATCH] =?UTF-8?q?=E2=9A=A1=EF=B8=8F=20Speed=20up=20=5Fhamming=5F?=
 =?UTF-8?q?distance=20by=2050%?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../langchain/evaluation/embedding_distance/base.py          | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/libs/langchain/langchain/evaluation/embedding_distance/base.py b/libs/langchain/langchain/evaluation/embedding_distance/base.py
index 2010d1d5e27f1..9ab740e701b56 100644
--- a/libs/langchain/langchain/evaluation/embedding_distance/base.py
+++ b/libs/langchain/langchain/evaluation/embedding_distance/base.py
@@ -166,7 +166,8 @@ def _chebyshev_distance(a: np.ndarray, b: np.ndarray) -> np.floating:
 
     @staticmethod
     def _hamming_distance(a: np.ndarray, b: np.ndarray) -> np.floating:
-        """Compute the Hamming distance between two vectors.
+        """
+        Compute the Hamming distance between two vectors.
 
         Args:
             a (np.ndarray): The first vector.
@@ -175,7 +176,7 @@ def _hamming_distance(a: np.ndarray, b: np.ndarray) -> np.floating:
         Returns:
             np.floating: The Hamming distance.
         """
-        return np.mean(a != b)
+        return np.sum(a != b) / a.size
 
     def _compute_score(self, vectors: np.ndarray) -> float:
         """Compute the score based on the distance metric.