diff --git a/llama-index-core/llama_index/core/indices/vector_store/retrievers/retriever.py b/llama-index-core/llama_index/core/indices/vector_store/retrievers/retriever.py
index 9eb68ae496b549..3a429245494945 100644
--- a/llama-index-core/llama_index/core/indices/vector_store/retrievers/retriever.py
+++ b/llama-index-core/llama_index/core/indices/vector_store/retrievers/retriever.py
@@ -48,6 +48,7 @@ def __init__(
         node_ids: Optional[List[str]] = None,
         doc_ids: Optional[List[str]] = None,
         sparse_top_k: Optional[int] = None,
+        hybrid_top_k: Optional[int] = None,
         callback_manager: Optional[CallbackManager] = None,
         object_map: Optional[dict] = None,
         embed_model: Optional[BaseEmbedding] = None,
@@ -67,6 +68,7 @@ def __init__(
         self._doc_ids = doc_ids
         self._filters = filters
         self._sparse_top_k = sparse_top_k
+        self._hybrid_top_k = hybrid_top_k
         self._kwargs: Dict[str, Any] = kwargs.get("vector_store_kwargs", {})
 
         callback_manager = callback_manager or CallbackManager()
@@ -126,6 +128,7 @@ def _build_vector_store_query(
             alpha=self._alpha,
             filters=self._filters,
             sparse_top_k=self._sparse_top_k,
+            hybrid_top_k=self._hybrid_top_k,
         )
 
     def _build_node_list_from_query_result(
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/llama_index/vector_stores/mongodb/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/llama_index/vector_stores/mongodb/base.py
index 3cfe7606d1ed60..99ed76f2d95433 100644
--- a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/llama_index/vector_stores/mongodb/base.py
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/llama_index/vector_stores/mongodb/base.py
@@ -229,6 +229,10 @@ def client(self) -> Any:
         return self._mongodb_client
 
     def _query(self, query: VectorStoreQuery) -> VectorStoreQueryResult:
+        hybrid_top_k = query.hybrid_top_k or query.similarity_top_k
+        sparse_top_k = query.sparse_top_k or query.similarity_top_k
+        dense_top_k = query.similarity_top_k
+
         if query.mode == VectorStoreQueryMode.DEFAULT:
             if not query.query_embedding:
                 raise ValueError("query_embedding in VectorStoreQueryMode.DEFAULT")
@@ -240,7 +244,7 @@ def _query(self, query: VectorStoreQuery) -> VectorStoreQueryResult:
                     query_vector=query.query_embedding,
                     search_field=self._embedding_key,
                     index_name=self._vector_index_name,
-                    limit=query.similarity_top_k,
+                    limit=dense_top_k,
                     filter=filter,
                     oversampling_factor=self._oversampling_factor,
                 ),
@@ -259,15 +263,11 @@ def _query(self, query: VectorStoreQuery) -> VectorStoreQueryResult:
                 index_name=self._fulltext_index_name,
                 operator="text",
                 filter=filter,
-                limit=query.similarity_top_k,
+                limit=sparse_top_k,
             )
             pipeline.append({"$set": {"score": {"$meta": "searchScore"}}})
 
         elif query.mode == VectorStoreQueryMode.HYBRID:
-            if query.hybrid_top_k is None:
-                raise ValueError(
-                    f"hybrid_top_k not set. You must use this, not similarity_top_k in hybrid mode."
-                )
             # Combines Vector and Full-Text searches with Reciprocal Rank Fusion weighting
             logger.debug(f"Running {query.mode} mode query pipeline")
             scores_fields = ["vector_score", "fulltext_score"]
@@ -280,7 +280,7 @@ def _query(self, query: VectorStoreQuery) -> VectorStoreQueryResult:
                         query_vector=query.query_embedding,
                         search_field=self._embedding_key,
                         index_name=self._vector_index_name,
-                        limit=query.hybrid_top_k,
+                        limit=dense_top_k,
                         filter=filter,
                         oversampling_factor=self._oversampling_factor,
                     )
@@ -296,7 +296,7 @@ def _query(self, query: VectorStoreQuery) -> VectorStoreQueryResult:
                     index_name=self._fulltext_index_name,
                     operator="text",
                     filter=filter,
-                    limit=query.hybrid_top_k,
+                    limit=sparse_top_k,
                 )
                 text_pipeline.extend(reciprocal_rank_stage("fulltext_score"))
                 combine_pipelines(pipeline, text_pipeline, self._collection.name)
@@ -306,7 +306,7 @@ def _query(self, query: VectorStoreQuery) -> VectorStoreQueryResult:
                 query.alpha or 0.5
             )  # If no alpha is given, equal weighting is applied
             pipeline += final_hybrid_stage(
-                scores_fields=scores_fields, limit=query.hybrid_top_k, alpha=alpha
+                scores_fields=scores_fields, limit=hybrid_top_k, alpha=alpha
             )
 
             # Remove embeddings unless requested.
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/pyproject.toml
index 817f918190493c..1fd3e6c1c454b7 100644
--- a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/pyproject.toml
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/pyproject.toml
@@ -29,7 +29,7 @@ exclude = ["**/BUILD"]
 license = "MIT"
 name = "llama-index-vector-stores-mongodb"
 readme = "README.md"
-version = "0.2.1"
+version = "0.3.0"
 
 [tool.poetry.dependencies]
 python = ">=3.8.1,<4.0"