diff --git a/libs/langchain-mongodb/langchain_mongodb/vectorstores.py b/libs/langchain-mongodb/langchain_mongodb/vectorstores.py
index 288ab629..e50ea8a3 100644
--- a/libs/langchain-mongodb/langchain_mongodb/vectorstores.py
+++ b/libs/langchain-mongodb/langchain_mongodb/vectorstores.py
@@ -877,6 +877,26 @@ def similarity_search_by_vector(
         *args: Any,
         **kwargs: Any,
     ) -> list[Document]:
+        """Return MongoDB documents most similar to the given query vector.
+
+        Atlas Vector Search eliminates the need to run a separate
+        search system alongside your database.
+
+         Args:
+            query_vector: Embedding vector to search for.
+            k: (Optional) number of documents to return. Defaults to 4.
+            pre_filter: List of MQL match expressions comparing an indexed field
+            post_filter_pipeline: (Optional) Pipeline of MongoDB aggregation stages
+                to filter/process results after $vectorSearch.
+            oversampling_factor: Multiple of k used when generating number of candidates
+                at each step in the HNSW Vector Search.
+            include_embeddings: If True, the embedding vector of each result
+                will be included in metadata.
+            kwargs: Additional arguments are specific to the search_type
+
+        Returns:
+            List of documents most similar to the query vector.
+        """
         tuple_list = self.vector_store._similarity_search_with_score(
             query_vector,
             *args,