Spring Data 2025.0 Release Notes

General Themes

Vector abstraction to represent AI embeddings

Participating Modules

Details

Spring Data Build - 3.5
Spring Data Commons - 3.5
Spring Data JPA - 3.5
Spring Data Relational - 3.5
Spring Data MongoDB - 4.5
Spring Data Neo4j - 7.5
Spring Data Elasticsearch - 5.5
Spring Data Couchbase - 5.5
Spring Data for Apache Cassandra - 4.5
Spring Data Redis - 3.5
Spring Data REST - 4.5
Spring Data KeyValue - 3.5
Spring Data LDAP - 3.5

New and Noteworthy

Spring Data Commons - 3.5

Mark pure Repository SpEL Components for Removal

After introducing support for Value Expressions, we now no longer require components that only support SpEL without following the newly introduced Value Expressions API. Therefore, we’re deprecating QueryMethodEvaluationContextProvider (and its reactive variants), and SpelEvaluator for removal in the next major release.

Please migrate off this API towards their Value Expression replacements.

Vector domain type

A growing number of databases have support for vector data mainly in the form of an array of floating-point numbers or quantized int8 (8-bit-integer). The newly introduced Vector type allows abstracting the underlying values within the domain model to simplify declaration, portability, and default storage options.

class Article {

    Vector embedding;
}

Article article = new Article();
article.setEmbedding(Vector.of(model.getVectorOutput()));

Spring Data JPA - 3.5

Spring Data Relational - 3.5

Spring Data MongoDB - 4.5

Vector Search support

MongoDB Vector Search uses a $vectorSearch aggregation stage to run queries against specialized indexes that can be created using the new VectorIndex via SearchIndexOperations. The VectorSearchOperation can be part of an aggregation pipeline to do similarity search against vector data.

VectorIndex index = new VectorIndex("vector_index")
  .addVector("embedding"), vector -> vector.dimensions(1536).similarity(COSINE))
  .addFilter("year");

mongoTemplate.searchIndexOps(Movie.class)
  .createIndex(index);

// ...

VectorSearchOperation search = VectorSearchOperation.search("vector_index")
  .path("embedding")
  .vector( ... )
  .numCandidates(150)
  .limit(10)
  .quantization(SCALAR)
  .withSearchScore("score");

AggregationResults<MovieWithSearchScore> results = mongoTemplate
  .aggregate(newAggregation(Movie.class, search), MovieWithSearchScore.class);

Spring Data Neo4j - 7.5

Spring Data Elasticsearch - 5.5

Spring Data Couchbase - 5.5

Spring Data for Apache Cassandra - 4.5

Upgrade to Cassandra 5

We’ve upgraded the Cassandra driver to version 4.19 and we’re building against Cassandra 5.0.

Support for Storage-Attached Indexes

Spring Data Cassandra ships with support for Storage-Attached Indexes. Specifically, we provide programmatic interfaces to specify and generate CQL for CREATE INDEX … USING 'sai' and the @SaiIndexed annotation for text and vector index definitions.

Vector Search support

With introducing the Spring Data Vector type, we now allow using vector data types in mapped entities. We support both, Cassandra’s CqlVector and our Vector type to insert and query for vector data. Cassandra uses vectors in queries for similarity and ordering.

@Table
class CommentSearch {

	String comment;

	float similarity;
}

@Table
class Comments {

	@Id UUID id;
	String comment;

	@VectorType(dimensions = 5)
	@SaiIndexed Vector vector;
}

Vector vector = Vector.of(/* from your Embedding */);
Columns columns = Columns.empty().include("comment").select("vector",
		it -> it.similarity(vector).cosine().as("similarity"));
Query query = Query.select(columns).limit(3).sort(VectorSort.ann("vector", vector));

List<CommentSearch> result = template.query(Comments.class).as(CommentSearch.class).matching(query).all();

Spring Data Redis - 3.5

Spring Data KeyValue - 3.5

Support for `Not`, `NotIn`, and `NotLike` query keywords

Map-based repositories now support Not, NotIn, and NotLike keywords when using query derivation.

interface PersonRepository extends Repository<Person, String> {

  Person findByFirstnameNot(String firstname);

  Person findByFirstnameNotLike(String firstname);

  Person findByFirstnameNotIn(List<String> in);
}