diff --git a/src/langchain_google_cloud_sql_pg/vectorstore.py b/src/langchain_google_cloud_sql_pg/vectorstore.py
index 964e9df9..cbe12767 100644
--- a/src/langchain_google_cloud_sql_pg/vectorstore.py
+++ b/src/langchain_google_cloud_sql_pg/vectorstore.py
@@ -22,7 +22,7 @@
 import numpy as np
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
-from langchain_core.vectorstores import VectorStore
+from langchain_core.vectorstores import VectorStore, utils
 from sqlalchemy.engine.row import RowMapping
 
 from .engine import PostgresEngine
@@ -788,7 +788,7 @@ async def amax_marginal_relevance_search_with_score_by_vector(
         fetch_k = fetch_k if fetch_k else self.fetch_k
         lambda_mult = lambda_mult if lambda_mult else self.lambda_mult
         embedding_list = [json.loads(row[self.embedding_column]) for row in results]
-        mmr_selected = maximal_marginal_relevance(
+        mmr_selected = utils.maximal_marginal_relevance(
             np.array(embedding, dtype=np.float32),
             embedding_list,
             k=k,
@@ -963,76 +963,3 @@ async def is_valid_index(
         """
         results = await self.engine._afetch(query)
         return bool(len(results) == 1)
-
-
-### The following is copied from langchain-community until it's moved into core
-
-Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
-
-
-def maximal_marginal_relevance(
-    query_embedding: np.ndarray,
-    embedding_list: list,
-    lambda_mult: float = 0.5,
-    k: int = 4,
-) -> List[int]:
-    """Calculate maximal marginal relevance."""
-    if min(k, len(embedding_list)) <= 0:
-        return []
-    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]])
-    while len(idxs) < min(k, len(embedding_list)):
-        best_score = -np.inf
-        idx_to_add = -1
-        similarity_to_selected = cosine_similarity(embedding_list, selected)
-        for i, query_score in enumerate(similarity_to_query):
-            if i in idxs:
-                continue
-            redundant_score = max(similarity_to_selected[i])
-            equation_score = (
-                lambda_mult * query_score - (1 - lambda_mult) * redundant_score
-            )
-            if equation_score > best_score:
-                best_score = equation_score
-                idx_to_add = i
-        idxs.append(idx_to_add)
-        selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
-    return idxs
-
-
-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]:
-        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}."
-        )
-    try:
-        import simsimd as simd  # type: ignore
-
-        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:
-        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
-
-
-### End code from langchain-community