dependency version, docs

Author: l-trotta

pom.xml

@@ -157,6 +157,7 @@
 		<pgvector.version>0.1.4</pgvector.version>
 		<sap.hanadb.version>2.20.11</sap.hanadb.version>
 		<postgresql.version>42.7.2</postgresql.version>
+		<elasticsearch-java.version>8.13.3</elasticsearch-java.version>
 		<milvus.version>2.3.4</milvus.version>
 		<pinecone.version>0.8.0</pinecone.version>
 		<fastjson.version>2.0.46</fastjson.version>

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

@@ -134,11 +134,18 @@ Properties starting with the `spring.ai.vectorstore.elasticsearch.*` prefix are
 
 |`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.dense-vector-indexing` | Whether to use dense vector indexing. | true
 |`spring.ai.vectorstore.elasticsearch.similarity` | The similarity function to use. | `cosine`
 |`spring.ai.vectorstore.elasticsearch.initialize-schema`| whether to initialize the required schema  | `false`
 |===
 
+The following similarity functions are available:
+
+* cosine
+* l2_norm
+* dot_product
+
+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.
+
 == Metadata Filtering
 
 You can leverage the generic, portable xref:api/vectordbs.adoc#metadata-filters[metadata filters] with Elasticsearch as well.
@@ -214,10 +221,11 @@ Read the link:https://www.elastic.co/guide/en/elasticsearch/client/java-api-clie
 ----
 @Bean
 public RestClient restClient() {
-    RestClientBuilder builder = RestClient.builder(new HttpHost("<host>", 9200, "http"));
-    Header[] defaultHeaders = new Header[] { new BasicHeader("Authorization", "Basic <encoded username and password>") };
-    builder.setDefaultHeaders(defaultHeaders);
-    return builder.build();
+    RestClient.builder(new HttpHost("<host>", 9200, "http"))
+        .setDefaultHeaders(new Header[]{
+            new BasicHeader("Authorization", "Basic <encoded username and password>")
+        })
+        .build();
 }
 ----
 

spring-ai-spring-boot-autoconfigure/pom.xml

@@ -260,6 +260,7 @@
 			<optional>true</optional>
 		</dependency>
 
+		<!-- Elasticsearch Vector Store-->
 		<dependency>
 			<groupId>org.springframework.ai</groupId>
 			<artifactId>spring-ai-elasticsearch-store</artifactId>
@@ -281,6 +282,12 @@
 			<optional>true</optional>
 		</dependency>
 
+		<dependency>
+			<groupId>co.elastic.clients</groupId>
+			<artifactId>elasticsearch-java</artifactId>
+			<version>${elasticsearch-java.version}</version>
+		</dependency>
+
 		<!-- test dependencies -->
 
 		<dependency>

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

@@ -28,8 +28,6 @@
 import org.springframework.context.annotation.Bean;
 import org.springframework.util.StringUtils;
 
-import java.util.Objects;
-
 /**
  * @author Eddú Meléndez
  * @author Wei Jiang

vector-stores/spring-ai-elasticsearch-store/pom.xml

@@ -35,7 +35,7 @@
 		<dependency>
 			<groupId>co.elastic.clients</groupId>
 			<artifactId>elasticsearch-java</artifactId>
-			<version>8.13.2</version>
+			<version>${elasticsearch-java.version}</version>
 		</dependency>
 
 		<!-- TESTING -->

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

@@ -16,8 +16,6 @@
 package org.springframework.ai.vectorstore;
 
 import co.elastic.clients.elasticsearch.ElasticsearchClient;
-import co.elastic.clients.elasticsearch._types.mapping.DenseVectorProperty;
-import co.elastic.clients.elasticsearch._types.mapping.Property;
 import co.elastic.clients.elasticsearch.core.BulkRequest;
 import co.elastic.clients.elasticsearch.core.BulkResponse;
 import co.elastic.clients.elasticsearch.core.SearchResponse;
@@ -145,7 +143,7 @@ public List<Document> similaritySearch(SearchRequest searchRequest) {
 					sr -> sr.index(options.getIndexName())
 						.knn(knn -> knn.queryVector(vectors)
 							.similarity(finalThreshold)
-							.k(searchRequest.getTopK())
+							.k((long) searchRequest.getTopK())
 							.field("embedding")
 							.numCandidates((long) (1.5 * searchRequest.getTopK()))
 							.filter(fl -> fl.queryString(