Adjust IndexKind and DistanceFunction defaults

Milder Hernandez Cagua · Milder Hernandez Cagua · commit a2bfa8b0ab05 · 2024-10-01T22:47:58.000-07:00
diff --git a/aiservices/openai/src/test/java/com/microsoft/semantickernel/aiservices/openai/chatcompletion/JsonSchemaTest.java b/aiservices/openai/src/test/java/com/microsoft/semantickernel/aiservices/openai/chatcompletion/JsonSchemaTest.java
@@ -18,7 +18,7 @@ public void jacksonGenerationTest() throws JsonProcessingException {
         Assertions.assertEquals("foo", format.getJsonSchema().getName());
 
         Assertions.assertTrue(format.getJsonSchema().getSchema()
-            .replaceAll("\n", "")
+            .replaceAll("\\r\\n|\\r|\\n", "")
             .replaceAll(" +", "")
             .contains(
                 "\"type\":\"object\",\"properties\":{\"bar\":{}}"));
diff --git a/api-test/integration-tests/src/test/java/com/microsoft/semantickernel/tests/connectors/memory/jdbc/Hotel.java b/api-test/integration-tests/src/test/java/com/microsoft/semantickernel/tests/connectors/memory/jdbc/Hotel.java
@@ -6,6 +6,7 @@
 import com.microsoft.semantickernel.data.vectorstorage.attributes.VectorStoreRecordKeyAttribute;
 import com.microsoft.semantickernel.data.vectorstorage.attributes.VectorStoreRecordVectorAttribute;
 import com.microsoft.semantickernel.data.vectorstorage.definition.DistanceFunction;
+import com.microsoft.semantickernel.data.vectorstorage.definition.IndexKind;
 
 import java.util.List;
 
@@ -37,7 +38,7 @@ public class Hotel {
     private final List<Float> dotProduct;
 
     @JsonProperty("indexedSummaryEmbedding")
-    @VectorStoreRecordVectorAttribute(dimensions = 8, indexKind = "hnsw", distanceFunction = DistanceFunction.EUCLIDEAN_DISTANCE)
+    @VectorStoreRecordVectorAttribute(dimensions = 8, indexKind = IndexKind.HNSW, distanceFunction = DistanceFunction.EUCLIDEAN_DISTANCE)
     private final List<Float> indexedEuclidean;
     @VectorStoreRecordDataAttribute
     private double rating;
diff --git a/api-test/integration-tests/src/test/java/com/microsoft/semantickernel/tests/connectors/memory/redis/Hotel.java b/api-test/integration-tests/src/test/java/com/microsoft/semantickernel/tests/connectors/memory/redis/Hotel.java
@@ -6,6 +6,8 @@
 import com.microsoft.semantickernel.data.vectorstorage.attributes.VectorStoreRecordKeyAttribute;
 import com.microsoft.semantickernel.data.vectorstorage.attributes.VectorStoreRecordVectorAttribute;
 import com.microsoft.semantickernel.data.vectorstorage.definition.DistanceFunction;
+import com.microsoft.semantickernel.data.vectorstorage.definition.IndexKind;
+
 import java.util.List;
 
 public class Hotel {
@@ -24,15 +26,15 @@ public class Hotel {
     private final String description;
 
     @JsonProperty("summaryEmbedding1")
-    @VectorStoreRecordVectorAttribute(dimensions = 8, distanceFunction = DistanceFunction.EUCLIDEAN_DISTANCE)
+    @VectorStoreRecordVectorAttribute(dimensions = 8, indexKind = IndexKind.HNSW, distanceFunction = DistanceFunction.EUCLIDEAN_DISTANCE)
     private final List<Float> euclidean;
 
     @JsonProperty("summaryEmbedding2")
-    @VectorStoreRecordVectorAttribute(dimensions = 8, distanceFunction = DistanceFunction.COSINE_DISTANCE)
+    @VectorStoreRecordVectorAttribute(dimensions = 8, indexKind = IndexKind.HNSW, distanceFunction = DistanceFunction.COSINE_DISTANCE)
     private final List<Float> cosineDistance;
 
     @JsonProperty("summaryEmbedding3")
-    @VectorStoreRecordVectorAttribute(dimensions = 8, distanceFunction = DistanceFunction.DOT_PRODUCT)
+    @VectorStoreRecordVectorAttribute(dimensions = 8, indexKind = IndexKind.HNSW, distanceFunction = DistanceFunction.DOT_PRODUCT)
     private final List<Float> dotProduct;
     @VectorStoreRecordDataAttribute
     private double rating;
diff --git a/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/InMemoryVolatileVectorStore.java b/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/InMemoryVolatileVectorStore.java
@@ -20,6 +20,7 @@
 import java.util.Map;
 import java.util.stream.Collectors;
 import com.microsoft.semantickernel.data.vectorstorage.definition.DistanceFunction;
+import com.microsoft.semantickernel.data.vectorstorage.definition.IndexKind;
 import reactor.core.publisher.Flux;
 import reactor.core.publisher.Mono;
 
@@ -44,7 +45,7 @@ static class GitHubFile {
         private final String description;
         @VectorStoreRecordDataAttribute
         private final String link;
-        @VectorStoreRecordVectorAttribute(dimensions = EMBEDDING_DIMENSIONS, indexKind = "Hnsw", distanceFunction = DistanceFunction.COSINE_DISTANCE)
+        @VectorStoreRecordVectorAttribute(dimensions = EMBEDDING_DIMENSIONS, indexKind = IndexKind.HNSW, distanceFunction = DistanceFunction.COSINE_DISTANCE)
         private final List<Float> embedding;
 
         public GitHubFile(
diff --git a/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithAzureAISearch.java b/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithAzureAISearch.java
@@ -28,6 +28,8 @@
 import java.util.List;
 import java.util.Map;
 import java.util.stream.Collectors;
+
+import com.microsoft.semantickernel.data.vectorstorage.definition.IndexKind;
 import reactor.core.publisher.Flux;
 import reactor.core.publisher.Mono;
 
@@ -58,7 +60,7 @@ static class GitHubFile {
         private final String description;
         @VectorStoreRecordDataAttribute
         private final String link;
-        @VectorStoreRecordVectorAttribute(dimensions = EMBEDDING_DIMENSIONS, indexKind = "Hnsw", distanceFunction = DistanceFunction.COSINE_DISTANCE)
+        @VectorStoreRecordVectorAttribute(dimensions = EMBEDDING_DIMENSIONS, indexKind = IndexKind.HNSW, distanceFunction = DistanceFunction.COSINE_DISTANCE)
         private final List<Float> embedding;
 
         public GitHubFile() {
diff --git a/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithJDBC.java b/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithJDBC.java
@@ -49,7 +49,7 @@ static class GitHubFile {
         private final String description;
         @VectorStoreRecordDataAttribute
         private final String link;
-        @VectorStoreRecordVectorAttribute(dimensions = EMBEDDING_DIMENSIONS, indexKind = "Hnsw", distanceFunction = DistanceFunction.COSINE_DISTANCE)
+        @VectorStoreRecordVectorAttribute(dimensions = EMBEDDING_DIMENSIONS, distanceFunction = DistanceFunction.COSINE_DISTANCE)
         private final List<Float> embedding;
 
         public GitHubFile() {
diff --git a/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithRedis.java b/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithRedis.java
@@ -28,6 +28,7 @@
 import java.util.stream.Collectors;
 
 import com.microsoft.semantickernel.data.vectorstorage.definition.DistanceFunction;
+import com.microsoft.semantickernel.data.vectorstorage.definition.IndexKind;
 import reactor.core.publisher.Flux;
 import reactor.core.publisher.Mono;
 import redis.clients.jedis.JedisPooled;
@@ -52,7 +53,7 @@ public static class GitHubFile {
         private final String description;
         @VectorStoreRecordDataAttribute
         private final String link;
-        @VectorStoreRecordVectorAttribute(dimensions = EMBEDDING_DIMENSIONS, indexKind = "Hnsw", distanceFunction = DistanceFunction.COSINE_DISTANCE)
+        @VectorStoreRecordVectorAttribute(dimensions = EMBEDDING_DIMENSIONS, indexKind = IndexKind.HNSW, distanceFunction = DistanceFunction.COSINE_DISTANCE)
         private final List<Float> embedding;
 
         public GitHubFile() {
diff --git a/semantickernel-api/src/test/java/com/microsoft/semantickernel/templateengine/handlebars/HandlebarsPromptTemplateTest.java b/semantickernel-api/src/test/java/com/microsoft/semantickernel/templateengine/handlebars/HandlebarsPromptTemplateTest.java
@@ -156,7 +156,7 @@ public void testSerializesObject() {
 
         String result = instance.renderAsync(Kernel.builder().build(), arguments, null)
             .block();
-        Assertions.assertEquals(expResult, result.replaceAll("\\n", ""));
+        Assertions.assertEquals(expResult, result.replaceAll("\\r\\n|\\r|\\n", ""));
     }
 
     @Test
diff --git a/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/connectors/data/azureaisearch/AzureAISearchVectorStoreCollectionCreateMapping.java b/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/connectors/data/azureaisearch/AzureAISearchVectorStoreCollectionCreateMapping.java
@@ -10,6 +10,8 @@
 import com.azure.search.documents.indexes.models.VectorSearchAlgorithmConfiguration;
 import com.azure.search.documents.indexes.models.VectorSearchAlgorithmMetric;
 import com.azure.search.documents.indexes.models.VectorSearchProfile;
+import com.microsoft.semantickernel.data.vectorstorage.definition.DistanceFunction;
+import com.microsoft.semantickernel.data.vectorstorage.definition.IndexKind;
 import com.microsoft.semantickernel.data.vectorstorage.definition.VectorStoreRecordDataField;
 import com.microsoft.semantickernel.data.vectorstorage.definition.VectorStoreRecordKeyField;
 import com.microsoft.semantickernel.data.vectorstorage.definition.VectorStoreRecordVectorField;
@@ -31,7 +33,7 @@ private static String getAlgorithmConfigName(VectorStoreRecordVectorField vector
 
     private static VectorSearchAlgorithmMetric getAlgorithmMetric(
         @Nonnull VectorStoreRecordVectorField vectorField) {
-        if (vectorField.getDistanceFunction() == null) {
+        if (vectorField.getDistanceFunction() == DistanceFunction.UNDEFINED) {
             return VectorSearchAlgorithmMetric.COSINE;
         }
 
@@ -50,7 +52,7 @@ private static VectorSearchAlgorithmMetric getAlgorithmMetric(
 
     private static VectorSearchAlgorithmConfiguration getAlgorithmConfig(
         @Nonnull VectorStoreRecordVectorField vectorField) {
-        if (vectorField.getIndexKind() == null) {
+        if (vectorField.getIndexKind() == IndexKind.UNDEFINED) {
             return new HnswAlgorithmConfiguration(getAlgorithmConfigName(vectorField))
                 .setParameters(new HnswParameters().setMetric(getAlgorithmMetric(vectorField)));
         }
diff --git a/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/connectors/data/jdbc/JDBCVectorStoreQueryProvider.java b/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/connectors/data/jdbc/JDBCVectorStoreQueryProvider.java
@@ -5,6 +5,7 @@
 import com.microsoft.semantickernel.data.vectorsearch.VectorSearchResult;
 import com.microsoft.semantickernel.data.vectorstorage.VectorStoreRecordMapper;
 import com.microsoft.semantickernel.data.vectorstorage.definition.DistanceFunction;
+import com.microsoft.semantickernel.data.vectorstorage.definition.IndexKind;
 import com.microsoft.semantickernel.data.vectorstorage.definition.VectorStoreRecordDefinition;
 import com.microsoft.semantickernel.data.vectorstorage.definition.VectorStoreRecordField;
 import com.microsoft.semantickernel.data.vectorstorage.definition.VectorStoreRecordVectorField;
@@ -263,7 +264,9 @@ public void createCollection(String collectionName,
 
         // No approximate search is supported in JDBCVectorStoreQueryProvider
         if (recordDefinition.getVectorFields().stream()
-            .anyMatch(field -> field.getIndexKind() != null)) {
+            .anyMatch(
+                field -> field.getIndexKind() != null && field.getIndexKind() != IndexKind.FLAT
+                    && field.getIndexKind() != IndexKind.UNDEFINED)) {
             LOGGER
                 .warn(String.format("Indexes are not supported in %s. Ignoring indexKind property.",
                     this.getClass().getName()));
@@ -532,9 +535,10 @@ public <Record> List<VectorSearchResult<Record>> search(String collectionName,
         List<Record> records = getRecordsWithFilter(collectionName, recordDefinition, mapper,
             new GetRecordOptions(true), filter, parameters);
 
-        DistanceFunction distanceFunction = vectorField.getDistanceFunction() == null
-            ? DistanceFunction.EUCLIDEAN_DISTANCE
-            : vectorField.getDistanceFunction();
+        DistanceFunction distanceFunction = vectorField
+            .getDistanceFunction() == DistanceFunction.UNDEFINED
+                ? DistanceFunction.EUCLIDEAN_DISTANCE
+                : vectorField.getDistanceFunction();
 
         return VectorOperations.exactSimilaritySearch(records, vector, vectorField,
             distanceFunction, options);
diff --git a/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/connectors/data/postgres/PostgreSQLVectorDistanceFunction.java b/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/connectors/data/postgres/PostgreSQLVectorDistanceFunction.java
@@ -5,7 +5,7 @@
 
 public enum PostgreSQLVectorDistanceFunction {
     L2("vector_l2_ops", "<->"), COSINE("vector_cosine_ops", "<=>"), INNER_PRODUCT("vector_ip_ops",
-        "<#>");
+        "<#>"), UNDEFINED(null, null);
 
     private final String value;
     private final String operator;
@@ -24,17 +24,15 @@ public String getOperator() {
     }
 
     public static PostgreSQLVectorDistanceFunction fromDistanceFunction(DistanceFunction function) {
-        if (function == null) {
-            return null;
-        }
-
         switch (function) {
             case EUCLIDEAN_DISTANCE:
                 return L2;
             case COSINE_DISTANCE:
                 return COSINE;
             case DOT_PRODUCT:
                 return INNER_PRODUCT;
+            case UNDEFINED:
+                return UNDEFINED;
             default:
                 throw new IllegalArgumentException("Unsupported distance function: " + function);
         }
diff --git a/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/connectors/data/postgres/PostgreSQLVectorIndexKind.java b/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/connectors/data/postgres/PostgreSQLVectorIndexKind.java
@@ -4,7 +4,7 @@
 import com.microsoft.semantickernel.data.vectorstorage.definition.IndexKind;
 
 public enum PostgreSQLVectorIndexKind {
-    HNSW("hnsw"), IVFFLAT("ivfflat");
+    HNSW("hnsw"), IVFFLAT("ivfflat"), UNDEFINED(null);
 
     private final String value;
 
@@ -17,15 +17,14 @@ public String getValue() {
     }
 
     public static PostgreSQLVectorIndexKind fromIndexKind(IndexKind indexKind) {
-        if (indexKind == null) {
-            return null;
-        }
-
         switch (indexKind) {
             case HNSW:
                 return HNSW;
-            case FLAT:
+            case IVFFLAT:
                 return IVFFLAT;
+            case FLAT:
+            case UNDEFINED:
+                return UNDEFINED;
             default:
                 throw new IllegalArgumentException("Unsupported index kind: " + indexKind);
         }
diff --git a/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/connectors/data/postgres/PostgreSQLVectorStoreQueryProvider.java b/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/connectors/data/postgres/PostgreSQLVectorStoreQueryProvider.java
@@ -164,12 +164,12 @@ private String createIndexForVectorField(String collectionName,
         PostgreSQLVectorDistanceFunction distanceFunction = PostgreSQLVectorDistanceFunction
             .fromDistanceFunction(vectorField.getDistanceFunction());
 
-        // If indexKind is not specified, no index is created
-        // and pgvector performs exact nearest neighbor search.
-        if (indexKind == null) {
+        // If there is no approximate search index associated to the vector field,
+        // there is no need to create an index and pgvector performs exact nearest neighbor search.
+        if (indexKind == PostgreSQLVectorIndexKind.UNDEFINED) {
             return null;
         }
-        if (distanceFunction == null) {
+        if (distanceFunction == PostgreSQLVectorDistanceFunction.UNDEFINED) {
             throw new SKException(
                 "Distance function is required for vector field: " + vectorField.getName());
         }
@@ -358,10 +358,11 @@ public <Record> List<VectorSearchResult<Record>> search(String collectionName,
         PostgreSQLVectorDistanceFunction distanceFunction = PostgreSQLVectorDistanceFunction
             .fromDistanceFunction(vectorField.getDistanceFunction());
 
-        // If indexKind is not specified, there is no index associated to the vector field
-        // and pgvector performs exact nearest neighbor search.
-        // If indexKind is specified, a distance function is required.
-        if (indexKind != null && distanceFunction == null) {
+        // If there is no approximate search index associated to the vector field,
+        // there is no index defined in the database and pgvector performs exact nearest neighbor search.
+        // If indexKind is defined, distance function is required.
+        if (indexKind != PostgreSQLVectorIndexKind.UNDEFINED
+            && distanceFunction == PostgreSQLVectorDistanceFunction.UNDEFINED) {
             throw new SKException(
                 "Distance function is required for vector field: " + vectorField.getName());
         }
diff --git a/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/connectors/data/redis/RedisVectorStoreCollectionCreateMapping.java b/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/connectors/data/redis/RedisVectorStoreCollectionCreateMapping.java
@@ -1,6 +1,8 @@
 // Copyright (c) Microsoft. All rights reserved.
 package com.microsoft.semantickernel.connectors.data.redis;
 
+import com.microsoft.semantickernel.data.vectorstorage.definition.DistanceFunction;
+import com.microsoft.semantickernel.data.vectorstorage.definition.IndexKind;
 import com.microsoft.semantickernel.data.vectorstorage.definition.VectorStoreRecordDataField;
 import com.microsoft.semantickernel.data.vectorstorage.definition.VectorStoreRecordField;
 import com.microsoft.semantickernel.data.vectorstorage.definition.VectorStoreRecordKeyField;
@@ -32,8 +34,8 @@ public class RedisVectorStoreCollectionCreateMapping {
 
     private static String getAlgorithmMetric(
         VectorStoreRecordVectorField vectorField) {
-        if (vectorField.getDistanceFunction() == null) {
-            return RedisVectorDistanceMetric.EUCLIDEAN;
+        if (vectorField.getDistanceFunction() == DistanceFunction.UNDEFINED) {
+            return RedisVectorDistanceMetric.COSINE;
         }
 
         switch (vectorField.getDistanceFunction()) {
@@ -51,7 +53,7 @@ private static String getAlgorithmMetric(
 
     private static Schema.VectorField.VectorAlgo getAlgorithmConfig(
         VectorStoreRecordVectorField vectorField) {
-        if (vectorField.getIndexKind() == null) {
+        if (vectorField.getIndexKind() == IndexKind.UNDEFINED) {
             return Schema.VectorField.VectorAlgo.HNSW;
         }
 
diff --git a/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/data/VolatileVectorStoreRecordCollection.java b/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/data/VolatileVectorStoreRecordCollection.java
@@ -276,9 +276,10 @@ public Mono<List<VectorSearchResult<Record>>> searchAsync(List<Float> vector,
                 : (VectorStoreRecordVectorField) recordDefinition
                     .getField(effectiveOptions.getVectorFieldName());
 
-            DistanceFunction distanceFunction = vectorField.getDistanceFunction() == null
-                ? DistanceFunction.EUCLIDEAN_DISTANCE
-                : vectorField.getDistanceFunction();
+            DistanceFunction distanceFunction = vectorField
+                .getDistanceFunction() == DistanceFunction.UNDEFINED
+                    ? DistanceFunction.EUCLIDEAN_DISTANCE
+                    : vectorField.getDistanceFunction();
 
             List<Record> records = VolatileVectorStoreCollectionSearchMapping.filterRecords(
                 new ArrayList<>(getCollection().values()), effectiveOptions.getVectorSearchFilter(),
diff --git a/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/data/vectorstorage/attributes/VectorStoreRecordVectorAttribute.java b/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/data/vectorstorage/attributes/VectorStoreRecordVectorAttribute.java
@@ -2,6 +2,8 @@
 package com.microsoft.semantickernel.data.vectorstorage.attributes;
 
 import com.microsoft.semantickernel.data.vectorstorage.definition.DistanceFunction;
+import com.microsoft.semantickernel.data.vectorstorage.definition.IndexKind;
+
 import javax.annotation.Nullable;
 import java.lang.annotation.ElementType;
 import java.lang.annotation.Retention;
@@ -28,12 +30,13 @@
     /**
      * Type of index to be used for the vector.
      */
-    String indexKind() default "";
+    @Nullable
+    IndexKind indexKind() default IndexKind.UNDEFINED;
 
     /**
      * Distance function to be used for to compute the distance between vectors.
      */
     @Nullable
-    DistanceFunction distanceFunction();
+    DistanceFunction distanceFunction() default DistanceFunction.UNDEFINED;
 
 }
diff --git a/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/data/vectorstorage/definition/DistanceFunction.java b/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/data/vectorstorage/definition/DistanceFunction.java
@@ -17,7 +17,11 @@ public enum DistanceFunction {
     /**
      * Euclidean distance function. Also known as L2 distance.
      */
-    EUCLIDEAN_DISTANCE("euclidean");
+    EUCLIDEAN_DISTANCE("euclidean"),
+    /**
+     * No distance function specified. It will default to the database's default distance function.
+     */
+    UNDEFINED(null);
 
     private final String value;
 
@@ -28,24 +32,4 @@ public enum DistanceFunction {
     public String getValue() {
         return value;
     }
-
-    /**
-     * Converts a string to a DistanceFunction.
-     * If the string is null or empty, the method returns DistanceFunction.COSINE_SIMILARITY.
-     *
-     * @param text the string to convert
-     * @return the DistanceFunction
-     */
-    public static DistanceFunction fromString(String text) {
-        if (text == null || text.isEmpty()) {
-            return null;
-        }
-
-        for (DistanceFunction b : DistanceFunction.values()) {
-            if (b.value.equalsIgnoreCase(text)) {
-                return b;
-            }
-        }
-        throw new IllegalArgumentException("No distance function with value " + text + " found");
-    }
 }
diff --git a/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/data/vectorstorage/definition/IndexKind.java b/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/data/vectorstorage/definition/IndexKind.java
diff --git a/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/data/vectorstorage/definition/VectorStoreRecordDefinition.java b/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/data/vectorstorage/definition/VectorStoreRecordDefinition.java
diff --git a/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/data/vectorstorage/definition/VectorStoreRecordVectorField.java b/semantickernel-experimental/src/main/java/com/microsoft/semantickernel/data/vectorstorage/definition/VectorStoreRecordVectorField.java

Original file line number	Diff line number	Diff line change
`@@ -156,7 +156,7 @@ public void testSerializesObject() {`
`156`	`156`
`157`	`157`	`String result = instance.renderAsync(Kernel.builder().build(), arguments, null)`
`158`	`158`	`.block();`
`159`		`- Assertions.assertEquals(expResult, result.replaceAll("\\n", ""));`
	`159`	`+ Assertions.assertEquals(expResult, result.replaceAll("\\r\\n\|\\r\|\\n", ""));`
`160`	`160`	`}`
`161`	`161`
`162`	`162`	`@Test`