diff --git a/solutions/search/vector.md b/solutions/search/vector.md
index 83b55fbffe..de65d40a76 100644
--- a/solutions/search/vector.md
+++ b/solutions/search/vector.md
@@ -29,7 +29,7 @@ Here's a quick reference overview of vector search field types and queries avail
 
 ## Dense vector search
 
-Dense neural embeddings capture semantic meaning by translating content into fixed-length vectors of floating-point bumbers. Similar content maps to nearby points in the vector space, making them ideal for:
+Dense neural embeddings capture semantic meaning by translating content into fixed-length vectors of floating-point numbers. Similar content maps to nearby points in the vector space, making them ideal for:
 - Finding semantically similar content
 - Matching questions with answers
 - Image similarity search
@@ -45,4 +45,4 @@ The sparse vector approach uses the ELSER model to expand content with semantica
 - Domain-specific search
 - Large-scale deployments
 
-[Learn more about sparse vector search with ELSER](vector/sparse-vector.md).
\ No newline at end of file
+[Learn more about sparse vector search with ELSER](vector/sparse-vector.md).