diff --git a/libs/community/langchain_community/vectorstores/cratedb/base.py b/libs/community/langchain_community/vectorstores/cratedb/base.py
index e9109764ecfc7e..c74b1c652b36c5 100644
--- a/libs/community/langchain_community/vectorstores/cratedb/base.py
+++ b/libs/community/langchain_community/vectorstores/cratedb/base.py
@@ -466,10 +466,10 @@ def _euclidean_relevance_score_fn(similarity: float) -> float:
         #  others are not!)
         # - embedding dimensionality
         # - etc.
-        # This function converts the euclidean norm of normalized embeddings
+        # This function converts the Euclidean norm of normalized embeddings
         # (0 is most similar, sqrt(2) most dissimilar)
         # to a similarity function (0 to 1)
 
         # Original:
         # return 1.0 - distance / math.sqrt(2)
-        return similarity / math.sqrt(2)
+        return similarity
diff --git a/libs/community/tests/integration_tests/vectorstores/test_cratedb.py b/libs/community/tests/integration_tests/vectorstores/test_cratedb.py
index acc03547fe5650..52aad4a0a85371 100644
--- a/libs/community/tests/integration_tests/vectorstores/test_cratedb.py
+++ b/libs/community/tests/integration_tests/vectorstores/test_cratedb.py
@@ -470,9 +470,9 @@ def test_cratedb_relevance_score() -> None:
     output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
     # Original score values: 1.0, 0.9996744261675065, 0.9986996093328621
     assert output == [
-        (Document(page_content="foo", metadata={"page": "0"}), 0.7071067811865475),
-        (Document(page_content="bar", metadata={"page": "1"}), 0.35355339059327373),
-        (Document(page_content="baz", metadata={"page": "2"}), 0.1414213562373095),
+        (Document(page_content="foo", metadata={"page": "0"}), 1.0),
+        (Document(page_content="bar", metadata={"page": "1"}), 0.5),
+        (Document(page_content="baz", metadata={"page": "2"}), 0.2),
     ]