diff --git a/libs/community/langchain_community/vectorstores/supabase.py b/libs/community/langchain_community/vectorstores/supabase.py
index b3f84c65eb9..70b45eee02c 100644
--- a/libs/community/langchain_community/vectorstores/supabase.py
+++ b/libs/community/langchain_community/vectorstores/supabase.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import uuid
+import warnings
 from itertools import repeat
 from typing import (
     TYPE_CHECKING,
@@ -204,7 +205,7 @@ class SupabaseVectorStore(VectorStore):
     ) -> List[Tuple[Document, float]]:
         vector = self._embedding.embed_query(query)
         return self.similarity_search_by_vector_with_relevance_scores(
-            vector, k=k, filter=filter
+            vector, k=k, filter=filter, **kwargs
         )
 
     def match_args(
@@ -221,6 +222,7 @@ class SupabaseVectorStore(VectorStore):
         k: int,
         filter: Optional[Dict[str, Any]] = None,
         postgrest_filter: Optional[str] = None,
+        score_threshold: Optional[float] = None,
     ) -> List[Tuple[Document, float]]:
         match_documents_params = self.match_args(query, filter)
         query_builder = self._client.rpc(self.query_name, match_documents_params)
@@ -246,6 +248,18 @@ class SupabaseVectorStore(VectorStore):
             if search.get("content")
         ]
 
+        if score_threshold is not None:
+            match_result = [
+                (doc, similarity)
+                for doc, similarity in match_result
+                if similarity >= score_threshold
+            ]
+            if len(match_result) == 0:
+                warnings.warn(
+                    "No relevant docs were retrieved using the relevance score"
+                    f" threshold {score_threshold}"
+                )
+
         return match_result
 
     def similarity_search_by_vector_returning_embeddings(