Review findings.

Author: mp911de

spring-data-mongodb/src/main/java/org/springframework/data/mongodb/repository/VectorSearch.java

@@ -53,11 +53,13 @@
 public @interface VectorSearch {
 
 	/**
-	 * Configuration whether to use ANN or ENN for the search. ANN is the default.
+	 * Configuration whether to use
+	 * {@link org.springframework.data.mongodb.core.aggregation.VectorSearchOperation.SearchType#ANN} or
+	 * {@link org.springframework.data.mongodb.core.aggregation.VectorSearchOperation.SearchType#ENN} for the search.
 	 *
 	 * @return the search type to use.
 	 */
-	VectorSearchOperation.SearchType searchType() default VectorSearchOperation.SearchType.ENN;
+	VectorSearchOperation.SearchType searchType() default VectorSearchOperation.SearchType.DEFAULT;
 
 	/**
 	 * Name of the Atlas Vector Search index to use. Atlas Vector Search doesn't return results if you misspell the index
@@ -98,21 +100,23 @@
 	 * specify {@link #numCandidates}. Limit accepts Value Expressions. A Vector Search method cannot define both,
 	 * {@code limit()} and a {@link org.springframework.data.domain.Limit} parameter. Supports Value Expressions.
 	 *
-	 * @return number of documents to return in the results
+	 * @return number of documents to return in the results.
 	 */
 	String limit() default "";
 
 	/**
 	 * Number of nearest neighbors to use during the search. Value must be less than or equal to ({@code <=})
 	 * {@code 10000}. You can't specify a number less than the {@link #limit() number of documents to return}. We
 	 * recommend that you specify a number at least {@code 20} times higher than the {@link #limit() number of documents
-	 * to return} to increase accuracy. This over-request pattern is the recommended way to trade off latency and recall
-	 * in your ANN searches, and we recommend tuning this parameter based on your specific dataset size and query
-	 * requirements. Required if the query uses
-	 * {@link org.springframework.data.mongodb.core.aggregation.VectorSearchOperation.SearchType#ANN}. Supports Value
-	 * Expressions.
+	 * to return} to increase accuracy.
+	 * <p>
+	 * This over-request pattern is the recommended way to trade off latency and recall in your ANN searches, and we
+	 * recommend tuning this parameter based on your specific dataset size and query requirements. Required if the query
+	 * uses
+	 * {@link org.springframework.data.mongodb.core.aggregation.VectorSearchOperation.SearchType#ANN}/{@link org.springframework.data.mongodb.core.aggregation.VectorSearchOperation.SearchType#DEFAULT}.
+	 * Supports Value Expressions.
 	 *
-	 * @return number of documents to return in the results
+	 * @return number of nearest neighbors to use during the search.
 	 */
 	String numCandidates() default "";
 

spring-data-mongodb/src/main/java/org/springframework/data/mongodb/repository/query/VectorSearchDelegate.java

@@ -19,6 +19,7 @@
 import java.util.List;
 
 import org.bson.Document;
+import org.jspecify.annotations.Nullable;
 
 import org.springframework.data.domain.Limit;
 import org.springframework.data.domain.Range;
@@ -46,7 +47,6 @@
 import org.springframework.data.repository.query.ValueExpressionDelegate;
 import org.springframework.data.repository.query.parser.Part;
 import org.springframework.data.repository.query.parser.PartTree;
-import org.springframework.lang.Nullable;
 import org.springframework.util.StringUtils;
 
 /**
@@ -145,7 +145,8 @@ public QueryMetadata createQuery(ValueExpressionEvaluator evaluator, ResultProce
 			numCandidates = ((Number) evaluator.evaluate(this.numCandidatesExpression)).intValue();
 		} else if (this.numCandidates != null) {
 			numCandidates = this.numCandidates;
-		} else if (query.query().isLimited() && searchType == VectorSearchOperation.SearchType.ANN) {
+		} else if (query.query().isLimited() && (searchType == VectorSearchOperation.SearchType.ANN
+				|| searchType == VectorSearchOperation.SearchType.DEFAULT)) {
 
 			/*
 			MongoDB: We recommend that you specify a number at least 20 times higher than the number of documents to return (limit) to increase accuracy.
@@ -195,7 +196,7 @@ ScoringFunction getSimilarityFunction(MongoParameterAccessor accessor) {
 	 * @param scoringFunction
 	 */
 	public record QueryMetadata(String path, String scoreField, Query query, VectorSearchOperation.SearchType searchType,
-			Class<?> outputType, @org.jspecify.annotations.Nullable Integer numCandidates, ScoringFunction scoringFunction) {
+			Class<?> outputType, @Nullable Integer numCandidates, ScoringFunction scoringFunction) {
 
 		/**
 		 * Create the Aggregation Pipeline.
@@ -210,12 +211,10 @@ public List<AggregationOperation> getAggregationPipeline(MongoQueryMethod queryM
 			Vector vector = accessor.getVector();
 			Score score = accessor.getScore();
 			Range<Score> distance = accessor.getScoreRange();
-			int limit;
+			Limit limit = Limit.unlimited();
 
 			if (query.isLimited()) {
-				limit = query.getLimit();
-			} else {
-				limit = Math.max(1, numCandidates() != null ? numCandidates() / 20 : 1);
+				limit = Limit.of(query.getLimit());
 			}
 
 			List<AggregationOperation> stages = new ArrayList<>();

spring-data-mongodb/src/jmh/java/org/springframework/data/mongodb/repository/query/VectorSearchDelegateUnitTests.java renamed to spring-data-mongodb/src/test/java/org/springframework/data/mongodb/repository/query/VectorSearchDelegateUnitTests.java

@@ -20,7 +20,6 @@
 
 import java.lang.reflect.Method;
 
-import org.jetbrains.annotations.NotNull;
 import org.junit.jupiter.api.Test;
 
 import org.springframework.data.domain.Limit;
@@ -107,7 +106,6 @@ private MongoQueryMethod getMongoQueryMethod(Method method) {
 		return new MongoQueryMethod(method, metadata, new SpelAwareProxyProjectionFactory(), converter.getMappingContext());
 	}
 
-	@NotNull
 	private static MongoParametersParameterAccessor getAccessor(MongoQueryMethod queryMethod, Object... values) {
 		return new MongoParametersParameterAccessor(queryMethod, values);
 	}