Add builder pattern and refactor Elasticsearch store package

The changes introduce a fluent builder pattern for ElasticsearchVectorStore
configuration, making it easier to create and customize instances with
optional parameters. All Elasticsearch-related classes are moved to a
dedicated elasticsearch package for better organization.

Key changes:

* Add ElasticsearchVectorStore.builder() with comprehensive options
* Move classes to org.springframework.ai.vectorstore.elasticsearch package
* Deprecate old constructors in favor of builder pattern
* Add support for configurable batching strategies
* Enhance documentation with usage examples and best practices

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/elasticsearch.adoc

@@ -76,14 +76,11 @@ Alternatively you can opt-out the initialization and create the index manually u
 
 NOTE: this is a breaking change! In earlier versions of Spring AI, this schema initialization happened by default.
 
-
-
 Please have a look at the list of <<elasticsearchvector-properties,configuration parameters>> for the vector store to learn about the default values and configuration options.
 These properties can be also set by configuring the `ElasticsearchVectorStoreOptions` bean.
 
 Additionally, you will need a configured `EmbeddingModel` bean. Refer to the xref:api/embeddings.adoc#available-implementations[EmbeddingModel] section for more information.
 
-
 Now you can auto-wire the `ElasticsearchVectorStore` as a vector store in your application.
 
 [source,java]
@@ -97,7 +94,7 @@ List <Document> documents = List.of(
     new Document("The World is Big and Salvation Lurks Around the Corner"),
     new Document("You walk forward facing the past and you turn back toward the future.", Map.of("meta2", "meta2")));
 
-// Add the documents to Qdrant
+// Add the documents to Elasticsearch
 vectorStore.add(documents);
 
 // Retrieve documents similar to a query
@@ -117,34 +114,19 @@ spring:
     uris: <elasticsearch instance URIs>
     username: <elasticsearch username>
     password: <elasticsearch password>
-# API key if needed, e.g. OpenAI
   ai:
-    openai:
-      api:
-        key: <api-key>
+    vectorstore:
+      elasticsearch:
+        initialize-schema: true
+        index-name: custom-index
+        dimensions: 1536
+        similarity: cosine
+        batching-strategy: TOKEN_COUNT # Optional: Controls how documents are batched for embedding
 ----
 
-environment variables,
-
-[source,bash]
-----
-export SPRING_ELASTICSEARCH_URIS=<elasticsearch instance URIs>
-export SPRING_ELASTICSEARCH_USERNAME=<elasticsearch username>
-export SPRING_ELASTICSEARCH_PASSWORD=<elasticsearch password>
-# API key if needed, e.g. OpenAI
-export SPRING_AI_OPENAI_API_KEY=<api-key>
-----
-
-or can be a mix of those.
-For example, if you want to store your password as an environment variable but keep the rest in the plain `application.yml` file.
-
-NOTE: If you choose to create a shell script for ease in future work, be sure to run it prior to starting your application by "sourcing" the file, i.e. `source <your_script_name>.sh`.
-
-Spring Boot's auto-configuration feature for the Elasticsearch RestClient will create a bean instance that will be used by the `ElasticsearchVectorStore`.
-
 The Spring Boot properties starting with `spring.elasticsearch.*` are used to configure the Elasticsearch client:
 
-[stripes=even]
+[cols="2,5,1",stripes=even]
 |===
 |Property | Description | Default Value
 
@@ -160,23 +142,24 @@ The Spring Boot properties starting with `spring.elasticsearch.*` are used to co
 | `spring.elasticsearch.socket-timeout` | Socket timeout used when communicating with Elasticsearch. | `30s`
 |===
 
-Properties starting with the `spring.ai.vectorstore.elasticsearch.*` prefix are used to configure `ElasticsearchVectorStore`.
+Properties starting with `spring.ai.vectorstore.elasticsearch.*` are used to configure the `ElasticsearchVectorStore`:
 
-[stripes=even]
+[cols="2,5,1",stripes=even]
 |===
 |Property | Description | Default Value
 
-|`spring.ai.vectorstore.elasticsearch.initialize-schema`| Whether to initialize the required schema  | `false`
-|`spring.ai.vectorstore.elasticsearch.index-name` | The name of the index to store the vectors. | spring-ai-document-index
-|`spring.ai.vectorstore.elasticsearch.dimensions` | The number of dimensions in the vector. | 1536
-|`spring.ai.vectorstore.elasticsearch.similarity` | The similarity function to use. | `cosine`
+|`spring.ai.vectorstore.elasticsearch.initialize-schema`| Whether to initialize the required schema | `false`
+|`spring.ai.vectorstore.elasticsearch.index-name` | The name of the index to store the vectors | `spring-ai-document-index`
+|`spring.ai.vectorstore.elasticsearch.dimensions` | The number of dimensions in the vector | `1536`
+|`spring.ai.vectorstore.elasticsearch.similarity` | The similarity function to use | `cosine`
+|`spring.ai.vectorstore.elasticsearch.batching-strategy` | Strategy for batching documents when calculating embeddings. Options are `TOKEN_COUNT` or `FIXED_SIZE` | `TOKEN_COUNT`
 |===
 
 The following similarity functions are available:
 
-* cosine
-* l2_norm
-* dot_product
+* `cosine` - Default, suitable for most use cases. Measures cosine similarity between vectors.
+* `l2_norm` - Euclidean distance between vectors. Lower values indicate higher similarity.
+* `dot_product` - Best performance for normalized vectors (e.g., OpenAI embeddings).
 
 More details about each in the https://www.elastic.co/guide/en/elasticsearch/reference/master/dense-vector.html#dense-vector-params[Elasticsearch Documentation] on dense vectors.
 
@@ -206,7 +189,7 @@ vectorStore.similaritySearch(SearchRequest.defaults()
         .withTopK(TOP_K)
         .withSimilarityThreshold(SIMILARITY_THRESHOLD)
         .withFilterExpression(b.and(
-                b.in("john", "jill"),
+                b.in("author", "john", "jill"),
                 b.eq("article_type", "blog")).build()));
 ----
 
@@ -247,7 +230,6 @@ dependencies {
 }
 ----
 
-
 Create an Elasticsearch `RestClient` bean.
 Read the link:https://www.elastic.co/guide/en/elasticsearch/client/java-api-client/current/java-rest-low-usage-initialization.html[Elasticsearch Documentation] for more in-depth information about the configuration of a custom RestClient.
 
@@ -255,7 +237,7 @@ Read the link:https://www.elastic.co/guide/en/elasticsearch/client/java-api-clie
 ----
 @Bean
 public RestClient restClient() {
-    RestClient.builder(new HttpHost("<host>", 9200, "http"))
+    return RestClient.builder(new HttpHost("<host>", 9200, "http"))
         .setDefaultHeaders(new Header[]{
             new BasicHeader("Authorization", "Basic <encoded username and password>")
         })
@@ -263,19 +245,29 @@ public RestClient restClient() {
 }
 ----
 
-and then create the `ElasticsearchVectorStore` bean:
+Then create the `ElasticsearchVectorStore` bean using the builder pattern:
 
 [source,java]
 ----
 @Bean
-public ElasticsearchVectorStore vectorStore(EmbeddingModel embeddingModel, RestClient restClient) {
-    return new ElasticsearchVectorStore( restClient, embeddingModel);
+public VectorStore vectorStore(RestClient restClient, EmbeddingModel embeddingModel) {
+    ElasticsearchVectorStoreOptions options = new ElasticsearchVectorStoreOptions();
+    options.setIndexName("custom-index");    // Optional: defaults to "spring-ai-document-index"
+    options.setSimilarity(COSINE);           // Optional: defaults to COSINE
+    options.setDimensions(1536);             // Optional: defaults to model dimensions or 1536
+
+    return ElasticsearchVectorStore.builder()
+        .restClient(restClient)
+        .embeddingModel(embeddingModel)
+        .options(options)                     // Optional: use custom options
+        .initializeSchema(true)               // Optional: defaults to false
+        .batchingStrategy(new TokenCountBatchingStrategy()) // Optional: defaults to TokenCountBatchingStrategy
+        .build();
 }
 
-// This can be any EmbeddingModel implementation.
+// This can be any EmbeddingModel implementation
 @Bean
 public EmbeddingModel embeddingModel() {
     return new OpenAiEmbeddingModel(new OpenAiApi(System.getenv("OPENAI_API_KEY")));
 }
 ----
-

spring-ai-spring-boot-autoconfigure/src/main/java/org/springframework/ai/autoconfigure/vectorstore/elasticsearch/ElasticsearchVectorStoreAutoConfiguration.java

@@ -22,8 +22,8 @@ import org.elasticsearch.client.RestClient;
 import org.springframework.ai.embedding.BatchingStrategy;
 import org.springframework.ai.embedding.EmbeddingModel;
 import org.springframework.ai.embedding.TokenCountBatchingStrategy;
-import org.springframework.ai.vectorstore.ElasticsearchVectorStore;
-import org.springframework.ai.vectorstore.ElasticsearchVectorStoreOptions;
+import org.springframework.ai.vectorstore.elasticsearch.ElasticsearchVectorStore;
+import org.springframework.ai.vectorstore.elasticsearch.ElasticsearchVectorStoreOptions;
 import org.springframework.ai.vectorstore.observation.VectorStoreObservationConvention;
 import org.springframework.beans.factory.ObjectProvider;
 import org.springframework.boot.autoconfigure.AutoConfiguration;
@@ -73,9 +73,15 @@ public class ElasticsearchVectorStoreAutoConfiguration {
 			elasticsearchVectorStoreOptions.setSimilarity(properties.getSimilarity());
 		}
 
-		return new ElasticsearchVectorStore(elasticsearchVectorStoreOptions, restClient, embeddingModel,
-				properties.isInitializeSchema(), observationRegistry.getIfUnique(() -> ObservationRegistry.NOOP),
-				customObservationConvention.getIfAvailable(() -> null), batchingStrategy);
+		return ElasticsearchVectorStore.builder()
+			.restClient(restClient)
+			.options(elasticsearchVectorStoreOptions)
+			.embeddingModel(embeddingModel)
+			.initializeSchema(properties.isInitializeSchema())
+			.observationRegistry(observationRegistry.getIfUnique(() -> ObservationRegistry.NOOP))
+			.customObservationConvention(customObservationConvention.getIfAvailable(() -> null))
+			.batchingStrategy(batchingStrategy)
+			.build();
 	}
 
 }

spring-ai-spring-boot-autoconfigure/src/main/java/org/springframework/ai/autoconfigure/vectorstore/elasticsearch/ElasticsearchVectorStoreProperties.java

@@ -17,7 +17,7 @@
 package org.springframework.ai.autoconfigure.vectorstore.elasticsearch;
 
 import org.springframework.ai.autoconfigure.vectorstore.CommonVectorStoreProperties;
-import org.springframework.ai.vectorstore.SimilarityFunction;
+import org.springframework.ai.vectorstore.elasticsearch.SimilarityFunction;
 import org.springframework.boot.context.properties.ConfigurationProperties;
 
 /**

spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/vectorstore/elasticsearch/ElasticsearchVectorStoreAutoConfigurationIT.java

@@ -33,9 +33,9 @@ import org.springframework.ai.autoconfigure.openai.OpenAiAutoConfiguration;
 import org.springframework.ai.autoconfigure.retry.SpringAiRetryAutoConfiguration;
 import org.springframework.ai.document.Document;
 import org.springframework.ai.observation.conventions.VectorStoreProvider;
-import org.springframework.ai.vectorstore.ElasticsearchVectorStore;
+import org.springframework.ai.vectorstore.elasticsearch.ElasticsearchVectorStore;
 import org.springframework.ai.vectorstore.SearchRequest;
-import org.springframework.ai.vectorstore.SimilarityFunction;
+import org.springframework.ai.vectorstore.elasticsearch.SimilarityFunction;
 import org.springframework.ai.vectorstore.observation.VectorStoreObservationContext;
 import org.springframework.boot.autoconfigure.AutoConfigurations;
 import org.springframework.boot.autoconfigure.elasticsearch.ElasticsearchRestClientAutoConfiguration;

vector-stores/spring-ai-elasticsearch-store/src/main/java/org/springframework/ai/vectorstore/elasticsearch/ElasticsearchAiSearchFilterExpressionConverter.java

@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.springframework.ai.vectorstore;
+package org.springframework.ai.vectorstore.elasticsearch;
 
 import java.text.ParseException;
 import java.text.SimpleDateFormat;

vector-stores/spring-ai-elasticsearch-store/src/main/java/org/springframework/ai/vectorstore/elasticsearch/ElasticsearchVectorStore.java

@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.springframework.ai.vectorstore;
+package org.springframework.ai.vectorstore.elasticsearch;
 
 import java.io.IOException;
 import java.util.List;
@@ -48,23 +48,100 @@ import org.springframework.ai.embedding.TokenCountBatchingStrategy;
 import org.springframework.ai.model.EmbeddingUtils;
 import org.springframework.ai.observation.conventions.VectorStoreProvider;
 import org.springframework.ai.observation.conventions.VectorStoreSimilarityMetric;
+import org.springframework.ai.vectorstore.AbstractVectorStoreBuilder;
+import org.springframework.ai.vectorstore.SearchRequest;
 import org.springframework.ai.vectorstore.filter.Filter;
 import org.springframework.ai.vectorstore.filter.FilterExpressionConverter;
 import org.springframework.ai.vectorstore.observation.AbstractObservationVectorStore;
 import org.springframework.ai.vectorstore.observation.VectorStoreObservationContext;
-import org.springframework.ai.vectorstore.observation.VectorStoreObservationContext.Builder;
 import org.springframework.ai.vectorstore.observation.VectorStoreObservationConvention;
 import org.springframework.beans.factory.InitializingBean;
 import org.springframework.util.Assert;
 
 /**
- * The ElasticsearchVectorStore class implements the VectorStore interface and provides
- * functionality for managing and querying documents in Elasticsearch. It uses an
- * embedding model to generate vector representations of the documents and performs
- * similarity searches based on these vectors.
+ * Elasticsearch-based vector store implementation using the dense_vector field type.
  *
- * The ElasticsearchVectorStore class requires a RestClient and an EmbeddingModel to be
- * instantiated. It also supports optional initialization of the Elasticsearch schema.
+ * <p>
+ * The store uses an Elasticsearch index to persist vector embeddings along with their
+ * associated document content and metadata. The implementation leverages Elasticsearch's
+ * k-NN search capabilities for efficient similarity search operations.
+ * </p>
+ *
+ * <p>
+ * Features:
+ * </p>
+ * <ul>
+ * <li>Automatic schema initialization with configurable index creation</li>
+ * <li>Support for multiple similarity functions: Cosine, L2 Norm, and Dot Product</li>
+ * <li>Metadata filtering using Elasticsearch query strings</li>
+ * <li>Configurable similarity thresholds for search results</li>
+ * <li>Batch processing support with configurable strategies</li>
+ * <li>Observation and metrics support through Micrometer</li>
+ * </ul>
+ *
+ * <p>
+ * Basic usage example:
+ * </p>
+ * <pre>{@code
+ * ElasticsearchVectorStore vectorStore = ElasticsearchVectorStore.builder()
+ *     .restClient(restClient)
+ *     .embeddingModel(embeddingModel)
+ *     .initializeSchema(true)
+ *     .build();
+ *
+ * // Add documents
+ * vectorStore.add(List.of(
+ *     new Document("content1", Map.of("key1", "value1")),
+ *     new Document("content2", Map.of("key2", "value2"))
+ * ));
+ *
+ * // Search with filters
+ * List<Document> results = vectorStore.similaritySearch(
+ *     SearchRequest.query("search text")
+ *         .withTopK(5)
+ *         .withSimilarityThreshold(0.7)
+ *         .withFilterExpression("key1 == 'value1'")
+ * );
+ * }</pre>
+ *
+ * <p>
+ * Advanced configuration example:
+ * </p>
+ * <pre>{@code
+ * ElasticsearchVectorStoreOptions options = new ElasticsearchVectorStoreOptions();
+ * options.setIndexName("custom_vectors");
+ * options.setSimilarity(SimilarityFunction.dot_product);
+ * options.setDimensions(1536);
+ *
+ * ElasticsearchVectorStore vectorStore = ElasticsearchVectorStore.builder()
+ *     .restClient(restClient)
+ *     .embeddingModel(embeddingModel)
+ *     .options(options)
+ *     .initializeSchema(true)
+ *     .batchingStrategy(new TokenCountBatchingStrategy())
+ *     .build();
+ * }</pre>
+ *
+ * <p>
+ * Requirements:
+ * </p>
+ * <ul>
+ * <li>Elasticsearch 8.0 or later</li>
+ * <li>Index mapping with id (string), content (text), metadata (object), and embedding
+ * (dense_vector) fields</li>
+ * </ul>
+ *
+ * <p>
+ * Similarity Functions:
+ * </p>
+ * <ul>
+ * <li>cosine: Default, suitable for most use cases. Measures cosine similarity between
+ * vectors.</li>
+ * <li>l2_norm: Euclidean distance between vectors. Lower values indicate higher
+ * similarity.</li>
+ * <li>dot_product: Best performance for normalized vectors (e.g., OpenAI
+ * embeddings).</li>
+ * </ul>
  *
  * @author Jemin Huh
  * @author Wei Jiang
@@ -83,8 +160,6 @@ public class ElasticsearchVectorStore extends AbstractObservationVectorStore imp
 			SimilarityFunction.cosine, VectorStoreSimilarityMetric.COSINE, SimilarityFunction.l2_norm,
 			VectorStoreSimilarityMetric.EUCLIDEAN, SimilarityFunction.dot_product, VectorStoreSimilarityMetric.DOT);
 
-	private final EmbeddingModel embeddingModel;
-
 	private final ElasticsearchClient elasticsearchClient;
 
 	private final ElasticsearchVectorStoreOptions options;
@@ -95,34 +170,47 @@ public class ElasticsearchVectorStore extends AbstractObservationVectorStore imp
 
 	private final BatchingStrategy batchingStrategy;
 
+	@Deprecated(since = "1.0.0-M5", forRemoval = true)
 	public ElasticsearchVectorStore(RestClient restClient, EmbeddingModel embeddingModel, boolean initializeSchema) {
 		this(new ElasticsearchVectorStoreOptions(), restClient, embeddingModel, initializeSchema);
 	}
 
+	@Deprecated(since = "1.0.0-M5", forRemoval = true)
 	public ElasticsearchVectorStore(ElasticsearchVectorStoreOptions options, RestClient restClient,
 			EmbeddingModel embeddingModel, boolean initializeSchema) {
 		this(options, restClient, embeddingModel, initializeSchema, ObservationRegistry.NOOP, null,
 				new TokenCountBatchingStrategy());
 	}
 
+	@Deprecated(since = "1.0.0-M5", forRemoval = true)
 	public ElasticsearchVectorStore(ElasticsearchVectorStoreOptions options, RestClient restClient,
 			EmbeddingModel embeddingModel, boolean initializeSchema, ObservationRegistry observationRegistry,
 			VectorStoreObservationConvention customObservationConvention, BatchingStrategy batchingStrategy) {
 
-		super(observationRegistry, customObservationConvention);
+		this(builder().restClient(restClient)
+			.options(options)
+			.embeddingModel(embeddingModel)
+			.initializeSchema(initializeSchema)
+			.observationRegistry(observationRegistry)
+			.customObservationConvention(customObservationConvention)
+			.batchingStrategy(batchingStrategy));
+	}
+
+	protected ElasticsearchVectorStore(ElasticsearchBuilder builder) {
+		super(builder);
+
+		Assert.notNull(builder.restClient, "RestClient must not be null");
+
+		this.initializeSchema = builder.initializeSchema;
+		this.options = builder.options;
+		this.filterExpressionConverter = builder.filterExpressionConverter;
+		this.batchingStrategy = builder.batchingStrategy;
 
-		this.initializeSchema = initializeSchema;
-		Objects.requireNonNull(embeddingModel, "RestClient must not be null");
-		Objects.requireNonNull(embeddingModel, "EmbeddingModel must not be null");
 		String version = Version.VERSION == null ? "Unknown" : Version.VERSION.toString();
-		this.elasticsearchClient = new ElasticsearchClient(new RestClientTransport(restClient,
+		this.elasticsearchClient = new ElasticsearchClient(new RestClientTransport(builder.restClient,
 				new JacksonJsonpMapper(
 						new ObjectMapper().configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false))))
 			.withTransportOptions(t -> t.addHeader("user-agent", "spring-ai elastic-java/" + version));
-		this.embeddingModel = embeddingModel;
-		this.options = options;
-		this.filterExpressionConverter = new ElasticsearchAiSearchFilterExpressionConverter();
-		this.batchingStrategy = batchingStrategy;
 	}
 
 	@Override
@@ -297,4 +385,95 @@ public class ElasticsearchVectorStore extends AbstractObservationVectorStore imp
 	public record ElasticSearchDocument(String id, String content, Map<String, Object> metadata, float[] embedding) {
 	}
 
+	/**
+	 * Creates a new builder instance for ElasticsearchVectorStore.
+	 * @return a new ElasticsearchBuilder instance
+	 */
+	public static ElasticsearchBuilder builder() {
+		return new ElasticsearchBuilder();
+	}
+
+	public static class ElasticsearchBuilder extends AbstractVectorStoreBuilder<ElasticsearchBuilder> {
+
+		private RestClient restClient;
+
+		private ElasticsearchVectorStoreOptions options = new ElasticsearchVectorStoreOptions();
+
+		private boolean initializeSchema = false;
+
+		private BatchingStrategy batchingStrategy = new TokenCountBatchingStrategy();
+
+		private FilterExpressionConverter filterExpressionConverter = new ElasticsearchAiSearchFilterExpressionConverter();
+
+		/**
+		 * Sets the Elasticsearch REST client.
+		 * @param restClient the Elasticsearch REST client
+		 * @return the builder instance
+		 * @throws IllegalArgumentException if restClient is null
+		 */
+		public ElasticsearchBuilder restClient(RestClient restClient) {
+			Assert.notNull(restClient, "RestClient must not be null");
+			this.restClient = restClient;
+			return this;
+		}
+
+		/**
+		 * Sets the Elasticsearch vector store options.
+		 * @param options the vector store options to use
+		 * @return the builder instance
+		 * @throws IllegalArgumentException if options is null
+		 */
+		public ElasticsearchBuilder options(ElasticsearchVectorStoreOptions options) {
+			Assert.notNull(options, "options must not be null");
+			this.options = options;
+			return this;
+		}
+
+		/**
+		 * Sets whether to initialize the schema.
+		 * @param initializeSchema true to initialize schema, false otherwise
+		 * @return the builder instance
+		 */
+		public ElasticsearchBuilder initializeSchema(boolean initializeSchema) {
+			this.initializeSchema = initializeSchema;
+			return this;
+		}
+
+		/**
+		 * Sets the batching strategy for vector operations.
+		 * @param batchingStrategy the batching strategy to use
+		 * @return the builder instance
+		 * @throws IllegalArgumentException if batchingStrategy is null
+		 */
+		public ElasticsearchBuilder batchingStrategy(BatchingStrategy batchingStrategy) {
+			Assert.notNull(batchingStrategy, "batchingStrategy must not be null");
+			this.batchingStrategy = batchingStrategy;
+			return this;
+		}
+
+		/**
+		 * Sets the filter expression converter.
+		 * @param converter the filter expression converter to use
+		 * @return the builder instance
+		 * @throws IllegalArgumentException if converter is null
+		 */
+		public ElasticsearchBuilder filterExpressionConverter(FilterExpressionConverter converter) {
+			Assert.notNull(converter, "filterExpressionConverter must not be null");
+			this.filterExpressionConverter = converter;
+			return this;
+		}
+
+		/**
+		 * Builds the ElasticsearchVectorStore instance.
+		 * @return a new ElasticsearchVectorStore instance
+		 * @throws IllegalStateException if the builder is in an invalid state
+		 */
+		@Override
+		public ElasticsearchVectorStore build() {
+			validate();
+			return new ElasticsearchVectorStore(this);
+		}
+
+	}
+
 }

vector-stores/spring-ai-elasticsearch-store/src/main/java/org/springframework/ai/vectorstore/elasticsearch/ElasticsearchVectorStoreOptions.java

@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.springframework.ai.vectorstore;
+package org.springframework.ai.vectorstore.elasticsearch;
 
 /**
  * Provided Elasticsearch vector option configuration.

vector-stores/spring-ai-elasticsearch-store/src/main/java/org/springframework/ai/vectorstore/elasticsearch/SimilarityFunction.java

@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.springframework.ai.vectorstore;
+package org.springframework.ai.vectorstore.elasticsearch;
 
 /**
  * https://www.elastic.co/guide/en/elasticsearch/reference/master/dense-vector.html

vector-stores/spring-ai-elasticsearch-store/src/test/java/org/springframework/ai/vectorstore/elasticsearch/ElasticsearchAiSearchFilterExpressionConverterTest.java

@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.springframework.ai.vectorstore;
+package org.springframework.ai.vectorstore.elasticsearch;
 
 import java.util.Date;
 import java.util.List;

vector-stores/spring-ai-elasticsearch-store/src/test/java/org/springframework/ai/vectorstore/elasticsearch/ElasticsearchImage.java

@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.springframework.ai.vectorstore;
+package org.springframework.ai.vectorstore.elasticsearch;
 
 import org.testcontainers.utility.DockerImageName;
 

vector-stores/spring-ai-elasticsearch-store/src/test/java/org/springframework/ai/vectorstore/elasticsearch/ElasticsearchVectorStoreIT.java

@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.springframework.ai.vectorstore;
+package org.springframework.ai.vectorstore.elasticsearch;
 
 import java.io.IOException;
 import java.nio.charset.StandardCharsets;
@@ -51,6 +51,7 @@ import org.springframework.ai.document.Document;
 import org.springframework.ai.embedding.EmbeddingModel;
 import org.springframework.ai.openai.OpenAiEmbeddingModel;
 import org.springframework.ai.openai.api.OpenAiApi;
+import org.springframework.ai.vectorstore.SearchRequest;
 import org.springframework.boot.SpringBootConfiguration;
 import org.springframework.boot.autoconfigure.EnableAutoConfiguration;
 import org.springframework.boot.autoconfigure.jdbc.DataSourceAutoConfiguration;
@@ -376,7 +377,11 @@ class ElasticsearchVectorStoreIT {
 
 		@Bean("vectorStore_cosine")
 		public ElasticsearchVectorStore vectorStoreDefault(EmbeddingModel embeddingModel, RestClient restClient) {
-			return new ElasticsearchVectorStore(restClient, embeddingModel, true);
+			return ElasticsearchVectorStore.builder()
+				.restClient(restClient)
+				.embeddingModel(embeddingModel)
+				.initializeSchema(true)
+				.build();
 		}
 
 		@Bean("vectorStore_l2_norm")
@@ -384,7 +389,12 @@ class ElasticsearchVectorStoreIT {
 			ElasticsearchVectorStoreOptions options = new ElasticsearchVectorStoreOptions();
 			options.setIndexName("index_l2");
 			options.setSimilarity(SimilarityFunction.l2_norm);
-			return new ElasticsearchVectorStore(options, restClient, embeddingModel, true);
+			return ElasticsearchVectorStore.builder()
+				.restClient(restClient)
+				.embeddingModel(embeddingModel)
+				.initializeSchema(true)
+				.options(options)
+				.build();
 		}
 
 		@Bean("vectorStore_dot_product")
@@ -392,7 +402,12 @@ class ElasticsearchVectorStoreIT {
 			ElasticsearchVectorStoreOptions options = new ElasticsearchVectorStoreOptions();
 			options.setIndexName("index_dot_product");
 			options.setSimilarity(SimilarityFunction.dot_product);
-			return new ElasticsearchVectorStore(options, restClient, embeddingModel, true);
+			return ElasticsearchVectorStore.builder()
+				.restClient(restClient)
+				.embeddingModel(embeddingModel)
+				.initializeSchema(true)
+				.options(options)
+				.build();
 		}
 
 		@Bean

vector-stores/spring-ai-elasticsearch-store/src/test/java/org/springframework/ai/vectorstore/elasticsearch/ElasticsearchVectorStoreObservationIT.java

@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.springframework.ai.vectorstore;
+package org.springframework.ai.vectorstore.elasticsearch;
 
 import java.io.IOException;
 import java.nio.charset.StandardCharsets;
@@ -51,6 +51,8 @@ import org.springframework.ai.observation.conventions.VectorStoreProvider;
 import org.springframework.ai.observation.conventions.VectorStoreSimilarityMetric;
 import org.springframework.ai.openai.OpenAiEmbeddingModel;
 import org.springframework.ai.openai.api.OpenAiApi;
+import org.springframework.ai.vectorstore.SearchRequest;
+import org.springframework.ai.vectorstore.VectorStore;
 import org.springframework.ai.vectorstore.observation.DefaultVectorStoreObservationConvention;
 import org.springframework.ai.vectorstore.observation.VectorStoreObservationDocumentation.HighCardinalityKeyNames;
 import org.springframework.ai.vectorstore.observation.VectorStoreObservationDocumentation.LowCardinalityKeyNames;
@@ -67,6 +69,7 @@ import static org.hamcrest.Matchers.greaterThan;
 /**
  * @author Christian Tzolov
  * @author Thomas Vitale
+ * @author Soby Chacko
  */
 @Testcontainers
 @EnabledIfEnvironmentVariable(named = "OPENAI_API_KEY", matches = ".+")
@@ -205,8 +208,15 @@ public class ElasticsearchVectorStoreObservationIT {
 		@Bean
 		public ElasticsearchVectorStore vectorStoreDefault(EmbeddingModel embeddingModel, RestClient restClient,
 				ObservationRegistry observationRegistry) {
-			return new ElasticsearchVectorStore(new ElasticsearchVectorStoreOptions(), restClient, embeddingModel, true,
-					observationRegistry, null, new TokenCountBatchingStrategy());
+			return ElasticsearchVectorStore.builder()
+				.restClient(restClient)
+				.embeddingModel(embeddingModel)
+				.initializeSchema(true)
+				.options(new ElasticsearchVectorStoreOptions())
+				.observationRegistry(observationRegistry)
+				.customObservationConvention(null)
+				.batchingStrategy(new TokenCountBatchingStrategy())
+				.build();
 		}
 
 		@Bean