fix tests

Signed-off-by: AnnTian Shao <anntians@amazon.com>
opensearch-project · Feb 26, 2025 · f267557 · f267557
1 parent 8a1ac42
commit f267557
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Showing 2 changed files with 9 additions and 4 deletions.
diff --git a/src/test/java/org/opensearch/knn/index/engine/faiss/FaissMethodResolverTests.java b/src/test/java/org/opensearch/knn/index/engine/faiss/FaissMethodResolverTests.java
@@ -24,6 +24,7 @@
 import static org.opensearch.knn.common.KNNConstants.ENCODER_FLAT;
 import static org.opensearch.knn.common.KNNConstants.METHOD_ENCODER_PARAMETER;
 import static org.opensearch.knn.common.KNNConstants.METHOD_HNSW;
+import static org.opensearch.knn.common.KNNConstants.ENCODER_PARAMETER_PQ_M;
 
 public class FaissMethodResolverTests extends KNNTestCase {
 
@@ -270,10 +271,14 @@ public void testResolveMethod_whenInvalid_thenThrow() {
 
         );
 
-        Map<String, Object> parameters = Map.of("m", 3);
-
+        Map<String, Object> parameters = Map.of(
+            ENCODER_PARAMETER_PQ_M,
+            3,
+            METHOD_ENCODER_PARAMETER,
+            new MethodComponentContext("pq", Map.of())
+        );
         MethodComponentContext methodComponentContext = new MethodComponentContext(METHOD_HNSW, parameters);
-        final KNNMethodContext knnMethodContext = new KNNMethodContext(KNNEngine.FAISS, SpaceType.DEFAULT, methodComponentContext);
+        final KNNMethodContext knnMethodContext = new KNNMethodContext(KNNEngine.FAISS, SpaceType.INNER_PRODUCT, methodComponentContext);
 
         KNNMethodConfigContext knnMethodConfigContext = KNNMethodConfigContext.builder()
             .vectorDataType(VectorDataType.FLOAT)

diff --git a/src/test/java/org/opensearch/knn/plugin/action/RestTrainModelHandlerIT.java b/src/test/java/org/opensearch/knn/plugin/action/RestTrainModelHandlerIT.java
@@ -52,7 +52,7 @@ public void testTrainModel_fail_notEnoughData() throws Exception {
 
         // Create a training index and randomly ingest data into it
         createBasicKnnIndex(trainingIndexName, trainingFieldName, dimension);
-        int trainingDataCount = 4;
+        int trainingDataCount = 1;
         bulkIngestRandomVectors(trainingIndexName, trainingFieldName, trainingDataCount, dimension);
 
         // Call the train API with this definition: