From 25ec655e4fd134ca15bfdb9f7640769c7e811d02 Mon Sep 17 00:00:00 2001
From: Bagatur <22008038+baskaryan@users.noreply.github.com>
Date: Thu, 7 Sep 2023 10:04:49 -0700
Subject: [PATCH] supabase embedding usage fix (#10335)

Should be calling Embeddings.embed_query instead of embed_documents when
searching
---
 libs/langchain/langchain/vectorstores/supabase.py | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

diff --git a/libs/langchain/langchain/vectorstores/supabase.py b/libs/langchain/langchain/vectorstores/supabase.py
index d911965346..4214e085f6 100644
--- a/libs/langchain/langchain/vectorstores/supabase.py
+++ b/libs/langchain/langchain/vectorstores/supabase.py
@@ -168,10 +168,8 @@ class SupabaseVectorStore(VectorStore):
         filter: Optional[Dict[str, Any]] = None,
         **kwargs: Any,
     ) -> List[Document]:
-        vectors = self._embedding.embed_documents([query])
-        return self.similarity_search_by_vector(
-            vectors[0], k=k, filter=filter, **kwargs
-        )
+        vector = self._embedding.embed_query(query)
+        return self.similarity_search_by_vector(vector, k=k, filter=filter, **kwargs)
 
     def similarity_search_by_vector(
         self,
@@ -195,9 +193,9 @@ class SupabaseVectorStore(VectorStore):
         filter: Optional[Dict[str, Any]] = None,
         **kwargs: Any,
     ) -> List[Tuple[Document, float]]:
-        vectors = self._embedding.embed_documents([query])
+        vector = self._embedding.embed_query(query)
         return self.similarity_search_by_vector_with_relevance_scores(
-            vectors[0], k=k, filter=filter
+            vector, k=k, filter=filter
         )
 
     def match_args(
@@ -407,9 +405,9 @@ class SupabaseVectorStore(VectorStore):
         $$;
         ```
         """
-        embedding = self._embedding.embed_documents([query])
+        embedding = self._embedding.embed_query(query)
         docs = self.max_marginal_relevance_search_by_vector(
-            embedding[0], k, fetch_k, lambda_mult=lambda_mult
+            embedding, k, fetch_k, lambda_mult=lambda_mult
         )
         return docs