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Signed-off-by: Balasubramanian <[email protected]>
opensearch-project · Feb 11, 2025 · 8c64b30 · 8c64b30
1 parent 69f4e6f
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diff --git a/src/main/java/org/opensearch/knn/plugin/script/KNNScoringSpace.java b/src/main/java/org/opensearch/knn/plugin/script/KNNScoringSpace.java
@@ -154,7 +154,7 @@ protected BiFunction<float[], float[], Float> getScoringMethod(float[] processed
         protected BiFunction<float[], float[], Float> getScoringMethod(final float[] processedQuery, Version indexCreatedVersion) {
             SpaceType.COSINESIMIL.validateVector(processedQuery);
             float qVectorSquaredMagnitude = getVectorMagnitudeSquared(processedQuery);
-            if (indexCreatedVersion.onOrAfter(Version.V_2_19_0)) {
+            if (indexCreatedVersion.onOrAfter(Version.V_2_17_0)) {
                 // To be consistent, we will be using same formula used by lucene as mentioned below
                 // https://github.com/apache/lucene/blob/0494c824e0ac8049b757582f60d085932a890800/lucene/core/src/java/org/apache/lucene/index/VectorSimilarityFunction.java#L73
                 // for indices that are created on or after 2.19.0

diff --git a/src/test/java/org/opensearch/knn/index/NmslibIT.java b/src/test/java/org/opensearch/knn/index/NmslibIT.java
@@ -194,61 +194,6 @@ public void testEndToEnd() throws Exception {
         fail("Graphs are not getting evicted");
     }
 
-    public void testEndToEnd_withApproxAndExactSearch_inSameIndex_ForCosineSpaceType() throws Exception {
-        String indexName = "test-index-1";
-        String fieldName = "test-field-1";
-        SpaceType spaceType = SpaceType.COSINESIMIL;
-        Integer dimension = testData.indexData.vectors[0].length;
-
-        // Create an index
-        XContentBuilder builder = XContentFactory.jsonBuilder()
-            .startObject()
-            .startObject("properties")
-            .startObject(fieldName)
-            .field("type", "knn_vector")
-            .field("dimension", dimension)
-            .field(KNNConstants.METHOD_PARAMETER_SPACE_TYPE, spaceType.getValue())
-            .startObject(KNNConstants.KNN_METHOD)
-            .field(KNNConstants.NAME, KNNConstants.METHOD_HNSW)
-            .field(KNNConstants.KNN_ENGINE, KNNEngine.NMSLIB.getName())
-            .endObject()
-            .endObject()
-            .endObject()
-            .endObject();
-
-        Map<String, Object> mappingMap = xContentBuilderToMap(builder);
-        String mapping = builder.toString();
-
-        createKnnIndex(indexName, buildKNNIndexSettings(0), mapping);
-
-        // Index one document
-        addKnnDoc(indexName, randomAlphaOfLength(5), fieldName, Floats.asList(testData.indexData.vectors[0]).toArray());
-
-        // Assert we have the right number of documents in the index
-        refreshAllIndices();
-        assertEquals(1, getDocCount(indexName));
-        // update threshold setting to skip building graph
-        updateIndexSettings(indexName, Settings.builder().put(KNNSettings.INDEX_KNN_ADVANCED_APPROXIMATE_THRESHOLD, -1));
-        // add duplicate document with different id
-        addKnnDoc(indexName, randomAlphaOfLength(5), fieldName, Floats.asList(testData.indexData.vectors[0]).toArray());
-        assertEquals(2, getDocCount(indexName));
-        final int k = 2;
-        // search index
-        Response response = searchKNNIndex(
-            indexName,
-            KNNQueryBuilder.builder().fieldName(fieldName).vector(testData.queries[0]).k(k).build(),
-            k
-        );
-        String responseBody = EntityUtils.toString(response.getEntity());
-        List<KNNResult> knnResults = parseSearchResponse(responseBody, fieldName);
-        assertEquals(k, knnResults.size());
-
-        List<Float> actualScores = parseSearchResponseScore(responseBody, fieldName);
-
-        // both document should have identical score
-        assertEquals(actualScores.get(0), actualScores.get(1), 0.001);
-    }
-
     @SneakyThrows
     private void validateSearch(
         final String indexName,