Merge pull request #1845 from vespa-engine/kkraune/avoid-deprecations

use non-deprecated version

Author: kkraune

billion-scale-image-search/src/main/java/ai/vespa/examples/Centroids.java

@@ -124,7 +124,7 @@ public List<Centroid> prune(List<Centroid> centroids, double factor) {
     private Query buildNNQuery(int k, int extraK, Tensor queryVector, Duration timeout) {
         NearestNeighborItem nn = new NearestNeighborItem("reduced_vector", "q_reduced");
         nn.setAllowApproximate(true);
-        nn.setTargetNumHits(k);
+        nn.setTargetHits(k);
         nn.setHnswExploreAdditionalHits(extraK);
         Query query = new Query();
         query.setTimeout(timeout.toMillis());

billion-scale-vector-search/src/main/java/ai/vespa/examples/ClusteringComponent.java

@@ -145,7 +145,7 @@ public List<Centroid> prune(List<Centroid> centroids, double factor) {
     private Query buildNNQuery(int k, int extraK, Tensor queryVector, Duration timeout) {
         NearestNeighborItem nn = new NearestNeighborItem("vector", "q");
         nn.setAllowApproximate(true);
-        nn.setTargetNumHits(k);
+        nn.setTargetHits(k);
         nn.setHnswExploreAdditionalHits(extraK);
         Query query = new Query();
         query.setTimeout(timeout.toMillis());

news/app-6-recommendation-with-searchers/src/main/java/ai/vespa/example/UserProfileSearcher.java

@@ -25,7 +25,7 @@ public Result search(Query query, Execution execution) {
 
             // Create a new search using the user's embedding tensor
             NearestNeighborItem nn = new NearestNeighborItem("embedding", "user_embedding");
-            nn.setTargetNumHits(query.getHits());
+            nn.setTargetHits(query.getHits());
             nn.setAllowApproximate(true);
 
             //Note that we only override the query tree, other native request parameters are intact

news/app-7-parent-child/src/main/java/ai/vespa/example/UserProfileSearcher.java

@@ -25,7 +25,7 @@ public Result search(Query query, Execution execution) {
 
             // Create a new search using the user's embedding tensor
             NearestNeighborItem nn = new NearestNeighborItem("embedding", "user_embedding");
-            nn.setTargetNumHits(query.getHits());
+            nn.setTargetHits(query.getHits());
             nn.setAllowApproximate(true);
 
             query.getModel().getQueryTree().setRoot(nn);

text-image-search/src/main/java/ai/vespa/example/TextEmbeddingSearcher.java

@@ -43,7 +43,7 @@ public Result search(Query query, Execution execution) {
         // Set up the nearest neighbor retrieval
         NearestNeighborItem nn = new NearestNeighborItem("vit_b_32_image", "vit_b_32_text");
         nn.setAllowApproximate(true);
-        nn.setTargetNumHits(10);
+        nn.setTargetHits(10);
         nn.setHnswExploreAdditionalHits(100);
         query.getModel().getQueryTree().setRoot(nn);