diff --git a/demos/guru_scripts/docker/tutorial/4.x/VectorSearch.md b/demos/guru_scripts/docker/tutorial/4.x/VectorSearch.md
index f8bb447c..7cf38d8b 100644
--- a/demos/guru_scripts/docker/tutorial/4.x/VectorSearch.md
+++ b/demos/guru_scripts/docker/tutorial/4.x/VectorSearch.md
@@ -99,7 +99,7 @@ result = vectorSearch(VectorAttributes, EmbeddingConstant, K, optionalParam)
 |`VectorAttributes`	|A set of vector attributes we will search, the items should be in format **VertexType.VectorName**. E.g., `{Account.eb1, Phone.eb2}`.
 |`QueryEmbedding`	|The query embedding constant to search the top K most similar vectors.
 |`K`	|The top k cutoff--where K most similar vectors will be returned.
-|`optionalParam` |Optional, a map of params, including vertex candidate set, EF-- the exploration factor in HNSW algorithm, and global MapAccum storing top-k (vertex, distance score) pairs. E.g., `{candidate_set: vset1, ef: 20, distance_map: @@distmap}`.
+|`optionalParam` | A map of optional params, including vertex candidate set, EF-- the exploration factor in HNSW algorithm, and global MapAccum storing top-k (vertex, distance score) pairs. E.g., `{candidate_set: vset1, ef: 20, distance_map: @@distmap}`.
 
 ### Return
 Will return a vertex set