Add IT for neural sparse query + bert-uncased mbert-uncased analyzer (opensearch-project#1279)

* add it

Signed-off-by: zhichao-aws <[email protected]>

* change log

Signed-off-by: zhichao-aws <[email protected]>

---------

Signed-off-by: zhichao-aws <[email protected]>
Signed-off-by: yeonghyeonKo <[email protected]>

Author: zhichao-aws

CHANGELOG.md

@@ -6,7 +6,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 ## [Unreleased 3.x](https://github.com/opensearch-project/neural-search/compare/main...HEAD)
 
 ### Features
-- Implement analyzer based neural sparse query ([#1088](https://github.com/opensearch-project/neural-search/pull/1088))
+- Implement analyzer based neural sparse query ([#1088](https://github.com/opensearch-project/neural-search/pull/1088) [#1279](https://github.com/opensearch-project/neural-search/pull/1279))
 - [Semantic Field] Add semantic field mapper. ([#1225](https://github.com/opensearch-project/neural-search/pull/1225)).
 
 ### Enhancements

src/test/java/org/opensearch/neuralsearch/query/NeuralSparseQueryIT.java

@@ -100,6 +100,24 @@ public void testBasicQueryUsingQueryTextAndAnalyzer() {
             assertEquals("1", firstInnerHit.get("_id"));
             float expectedScore = 2 * computeExpectedScore(testRankFeaturesDoc, Map.of("hello", 1f, "world", 1f, "a", 1f, "b", 1f));
             assertEquals(expectedScore, objectToFloat(firstInnerHit.get("_score")), DELTA);
+
+            sparseEncodingQueryBuilder.analyzer("bert-uncased");
+            Map<String, Object> searchResponseAsMap2 = search(TEST_BASIC_INDEX_NAME, sparseEncodingQueryBuilder, 1);
+            Map<String, Object> firstInnerHit2 = getFirstInnerHit(searchResponseAsMap2);
+            expectedScore = 2 * computeExpectedScore(
+                testRankFeaturesDoc,
+                Map.of("hello", 6.937756538391113f, "world", 3.4208686351776123f, "a", 0.4022789001464844f, "b", 3.7727110385894775f)
+            );
+            assertEquals(expectedScore, objectToFloat(firstInnerHit2.get("_score")), DELTA);
+
+            sparseEncodingQueryBuilder.analyzer("mbert-uncased");
+            Map<String, Object> searchResponseAsMap3 = search(TEST_BASIC_INDEX_NAME, sparseEncodingQueryBuilder, 1);
+            Map<String, Object> firstInnerHit3 = getFirstInnerHit(searchResponseAsMap3);
+            expectedScore = 2 * computeExpectedScore(
+                testRankFeaturesDoc,
+                Map.of("hello", 3.848525285720825f, "world", 1.5188000202178955f, "a", 1.054316759109497f, "b", 2.0035440921783447f)
+            );
+            assertEquals(expectedScore, objectToFloat(firstInnerHit3.get("_score")), DELTA);
         } finally {
             wipeOfTestResources(TEST_BASIC_INDEX_NAME, null, null, null);
         }