diff --git a/core/vector_store/multi_vector_store.py b/core/vector_store/multi_vector_store.py
index db44960..9d504f0 100644
--- a/core/vector_store/multi_vector_store.py
+++ b/core/vector_store/multi_vector_store.py
@@ -181,7 +181,8 @@ class MultiVectorStore(BaseVectorStore):
                         """
                         CREATE OR REPLACE FUNCTION max_sim(document bit[], query bit[]) RETURNS double precision AS $$
                             WITH queries AS (
-                                SELECT row_number() OVER () AS query_number, * FROM (SELECT unnest(query) AS query) AS foo
+                                SELECT row_number() OVER () AS query_number, *
+                                FROM (SELECT unnest(query) AS query) AS foo
                             ),
                             documents AS (
                                 SELECT unnest(document) AS document
@@ -189,7 +190,8 @@ class MultiVectorStore(BaseVectorStore):
                             similarities AS (
                                 SELECT
                                     query_number,
-                                    1.0 - (bit_count(document # query)::float / greatest(bit_length(query), 1)::float) AS similarity
+                                    1.0 - (bit_count(document # query)::float /
+                                        greatest(bit_length(query), 1)::float) AS similarity
                                 FROM queries CROSS JOIN documents
                             ),
                             max_similarities AS (
@@ -217,10 +219,6 @@ class MultiVectorStore(BaseVectorStore):
         if isinstance(embeddings, list) and not isinstance(embeddings[0], np.ndarray):
             embeddings = np.array(embeddings)
 
-        # Add this check to ensure pgvector is registered for the connection
-        with self.get_connection() as conn:
-            register_vector(conn)
-
         return [Bit(embedding > 0) for embedding in embeddings]
 
     async def store_embeddings(self, chunks: List[DocumentChunk]) -> Tuple[bool, List[str]]:
@@ -230,21 +228,21 @@ class MultiVectorStore(BaseVectorStore):
             return True, []
 
         stored_ids = []
+        with self.get_connection() as conn:
+            for chunk in chunks:
+                # Ensure embeddings exist
+                if not hasattr(chunk, "embedding") or chunk.embedding is None:
+                    logger.error(f"Missing embeddings for chunk {chunk.document_id}-{chunk.chunk_number}")
+                    continue
 
-        for chunk in chunks:
-            # Ensure embeddings exist
-            if not hasattr(chunk, "embedding") or chunk.embedding is None:
-                logger.error(f"Missing embeddings for chunk {chunk.document_id}-{chunk.chunk_number}")
-                continue
+                # For multi-vector embeddings, we expect a list of vectors
+                embeddings = chunk.embedding
 
-            # For multi-vector embeddings, we expect a list of vectors
-            embeddings = chunk.embedding
+                # Create binary representation for each vector
+                binary_embeddings = self._binary_quantize(embeddings)
 
-            # Create binary representation for each vector
-            binary_embeddings = self._binary_quantize(embeddings)
+                # Insert into database with retry logic
 
-            # Insert into database with retry logic
-            with self.get_connection() as conn:
                 conn.execute(
                     """
                     INSERT INTO multi_vector_embeddings
@@ -260,7 +258,7 @@ class MultiVectorStore(BaseVectorStore):
                     ),
                 )
 
-            stored_ids.append(f"{chunk.document_id}-{chunk.chunk_number}")
+                stored_ids.append(f"{chunk.document_id}-{chunk.chunk_number}")
 
         logger.debug(f"{len(stored_ids)} vector embeddings added successfully!")
         return len(stored_ids) > 0, stored_ids