diff --git a/openviking/retrieve/hierarchical_retriever.py b/openviking/retrieve/hierarchical_retriever.py
index a0a54b2f..689f0b2c 100644
--- a/openviking/retrieve/hierarchical_retriever.py
+++ b/openviking/retrieve/hierarchical_retriever.py
@@ -108,13 +108,30 @@ async def retrieve(
 
         final_metadata_filter = {"op": "and", "conds": filters_to_merge}
 
+        if not await self.storage.collection_exists(collection):
+            logger.warning(f"[RecursiveSearch] Collection {collection} does not exist")
+            return QueryResult(
+                query=query,
+                matched_contexts=[],
+                searched_directories=[],
+            )
+
+        # Generate query vectors once to avoid duplicate embedding calls
+        query_vector = None
+        sparse_query_vector = None
+        if self.embedder:
+            result: EmbedResult = self.embedder.embed(query.query)
+            query_vector = result.dense_vector
+            sparse_query_vector = result.sparse_vector
+
         # Step 1: Determine starting directories based on context_type
         root_uris = self._get_root_uris_for_type(query.context_type)
 
         # Step 2: Global vector search to supplement starting points
         global_results = await self._global_vector_search(
-            query=query.query,
             collection=collection,
+            query_vector=query_vector,
+            sparse_query_vector=sparse_query_vector,
             limit=self.GLOBAL_SEARCH_TOPK,
             filter=final_metadata_filter,
         )
@@ -125,8 +142,10 @@ async def retrieve(
         # Step 4: Recursive search
         candidates = await self._recursive_search(
             query=query.query,
-            starting_points=starting_points,
             collection=collection,
+            query_vector=query_vector,
+            sparse_query_vector=sparse_query_vector,
+            starting_points=starting_points,
             limit=limit,
             mode=mode,
             threshold=effective_threshold,
@@ -145,21 +164,16 @@ async def retrieve(
 
     async def _global_vector_search(
         self,
-        query: str,
         collection: str,
+        query_vector: Optional[List[float]],
+        sparse_query_vector: Optional[Dict[str, float]],
         limit: int,
         filter: Optional[Dict[str, Any]] = None,
     ) -> List[Dict[str, Any]]:
         """Global vector search to locate initial directories."""
-        if not self.embedder:
-            return []
-        if not await self.storage.collection_exists(collection):
-            return []
-        result: EmbedResult = self.embedder.embed(query)
-        query_vector = result.dense_vector
         if not query_vector:
             return []
-        sparse_query_vector = result.sparse_vector or {}
+        sparse_query_vector = sparse_query_vector or {}
 
         global_filter = {
             "op": "and",
@@ -215,8 +229,10 @@ def _merge_starting_points(
     async def _recursive_search(
         self,
         query: str,
-        starting_points: List[Tuple[str, float]],
         collection: str,
+        query_vector: Optional[List[float]],
+        sparse_query_vector: Optional[Dict[str, float]],
+        starting_points: List[Tuple[str, float]],
         limit: int,
         mode: str,
         threshold: Optional[float] = None,
@@ -249,18 +265,7 @@ def merge_filter(base_filter: Dict, extra_filter: Optional[Dict]) -> Dict:
                 return base_filter
             return {"op": "and", "conds": [base_filter, extra_filter]}
 
-        # Generate query vectors
-        query_vector = None
-        sparse_query_vector = None
-
-        if self.embedder:
-            result: EmbedResult = self.embedder.embed(query)
-            query_vector = result.dense_vector
-            sparse_query_vector = result.sparse_vector
-
-        if not await self.storage.collection_exists(collection):
-            logger.warning(f"[RecursiveSearch] Collection {collection} does not exist")
-            return []
+        sparse_query_vector = sparse_query_vector or None
 
         collected: List[Dict[str, Any]] = []  # Collected results (directories and leaves)
         dir_queue: List[tuple] = []  # Priority queue: (-score, uri)