diff --git a/_search-plugins/knn/approximate-knn.md b/_search-plugins/knn/approximate-knn.md
index 90691b37..f01472e8 100644
--- a/_search-plugins/knn/approximate-knn.md
+++ b/_search-plugins/knn/approximate-knn.md
@@ -11,7 +11,7 @@ has_math: true
 
 The approximate k-NN method uses [nmslib's](https://github.com/nmslib/nmslib/) implementation of the Hierarchical Navigable Small World (HNSW) algorithm to power k-NN search. In this case, approximate means that for a given search, the neighbors returned are an estimate of the true k-nearest neighbors. Of the three methods, this method offers the best search scalability for large data sets. Generally speaking, once the data set gets into the hundreds of thousands of vectors, this approach is preferred.
 
-The k-NN plugin builds an HNSW graph of the vectors for each "knn-vector field"/ "Lucene segment" pair during indexing that can be used to efficiently find the k-nearest neighbors to a query vector during search. To learn more about Lucene segments, see the [Apache Lucene documentation](https://lucene.apache.org/core/{{site.lucene_version}}/core/org/apache/lucene/codecs/lucene87/package-summary.html#package.description). These graphs are loaded into native memory during search and managed by a cache. To learn more about pre-loading graphs into memory, refer to the [warmup API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api#warmup-operation). Additionally, you can see what graphs are already loaded in memory, which you can learn more about in the [stats API section]({{site.url}}{{site.baseurl}}/search-plugins/knn/api#stats).
+The k-NN plugin builds an HNSW graph of the vectors for each "knn-vector field"/ "Lucene segment" pair during indexing that can be used to efficiently find the k-nearest neighbors to a query vector during search. To learn more about Lucene segments, see the [Apache Lucene documentation](https://lucene.apache.org/core/8_11_1/core/org/apache/lucene/codecs/lucene87/package-summary.html#package.description). These graphs are loaded into native memory during search and managed by a cache. To learn more about pre-loading graphs into memory, refer to the [warmup API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api#warmup-operation). Additionally, you can see what graphs are already loaded in memory, which you can learn more about in the [stats API section]({{site.url}}{{site.baseurl}}/search-plugins/knn/api#stats).
 
 Because the graphs are constructed during indexing, it is not possible to apply a filter on an index and then use this search method. All filters are applied on the results produced by the approximate nearest neighbor search.