Update _vector-search/filter-search-knn/index.md

Co-authored-by: kolchfa-aws <[email protected]>
Signed-off-by: Doo Yong Kim <[email protected]>

Author: 0ctopus13prime

_vector-search/filter-search-knn/index.md

@@ -15,7 +15,8 @@ To refine vector search results, you can filter a vector search using one of the
 - [Efficient k-nearest neighbors (k-NN) filtering]({{site.url}}{{site.baseurl}}/vector-search/filter-search-knn/efficient-knn-filtering/): This approach applies filtering _during_ the vector search, as opposed to before or after the vector search, which ensures that `k` results are returned (if there are at least `k` results in total). This approach is supported by the following engines:
   - Lucene engine with a Hierarchical Navigable Small World (HNSW) algorithm (OpenSearch version 2.4 and later) 
   - Faiss engine with an HNSW algorithm (OpenSearch version 2.9 and later) or IVF algorithm (OpenSearch version 2.10 and later)
-    - With the Faiss engine and HNSW, [Lucene ACORN filtering optimization](https://github.com/apache/lucene/pull/14160) is applied during HNSW traversal when [memory-optimized search]({{site.url}}{{site.baseurl}}/vector-search/optimizing-storage/memory-optimized-search/) is enabled.
+     When using the Faiss engine and HNSW, [Lucene ACORN filtering optimization](https://github.com/apache/lucene/pull/14160) is applied during HNSW traversal when [memory-optimized search]({{site.url}}{{site.baseurl}}/vector-search/optimizing-storage/memory-optimized-search/) is enabled.
+     {: .note}
 
 -  [Post-filtering]({{site.url}}{{site.baseurl}}/vector-search/filter-search-knn/post-filtering/): Because it is performed after the vector search, this approach may return significantly fewer than `k` results for a restrictive filter. You can use the following two filtering strategies for this approach:
     - [Boolean post-filter]({{site.url}}{{site.baseurl}}/vector-search/filter-search-knn/post-filtering/#boolean-filter-with-ann-search): This approach runs an [approximate nearest neighbor (ANN)]({{site.url}}{{site.baseurl}}/search-plugins/knn/approximate-knn/) search and then applies a filter to the results. The two query parts are executed independently, and then the results are combined based on the query operator (`should`, `must`, and so on) provided in the query.