Apply suggestions from code review

Co-authored-by: kolchfa-aws <[email protected]>
Signed-off-by: Nathan Bower <[email protected]>

Author: natebower

_vector-search/filter-search-knn/efficient-knn-filtering.md

@@ -409,7 +409,7 @@ The ACORN filtering optimization modifies the baseline algorithm to score and ex
 
 The algorithm bypasses these optimizations entirely when filtering is minimal. By default, this threshold is 60%. Extended neighbor exploration occurs only if fewer than 90% of the current neighbors match the filter. 
 
-When [memory-optimized search]({{site.url}}{{site.baseurl}}/vector-search/optimizing-storage/memory-optimized-search/) is enabled, the efficient filter framework continues to apply filtering within HNSW. The ACORN filtering optimization is applied only when the number of filtered documents is 60% or less of the total number of documents in the current search space being considered by the HNSW algorithm.
+When [memory-optimized search]({{site.url}}{{site.baseurl}}/vector-search/optimizing-storage/memory-optimized-search/) is enabled, the efficient filter framework continues to apply filtering within HNSW. The ACORN filtering optimization is applied only when the number of filtered documents is 60% or fewer of the total number of documents in the current search space being considered by the HNSW algorithm.
 
 ## Constructing a filter
 

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

@@ -14,8 +14,8 @@ 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)
-     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.
+  - Faiss engine with an HNSW algorithm (OpenSearch version 2.9 and later) or IVF algorithm (OpenSearch version 2.10 and later). In OpenSearch version 3.1 and later, when using the Faiss engine and HNSW, the [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, the [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: