Improve ElasticsearchVectorStore,

 - add index configuration and add support for ES response error handling.
 - rename dims to dimension propety.
 - add property javadocs
 - improve the elasticsearch javadoc.

Author: wmz7year

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

@@ -6,20 +6,23 @@ link:https://www.elastic.co/elasticsearch[Elasticsearch] is an open source searc
 
 == Prerequisites
 
-* A running Elasticsearch instance. The following options are available:
-** link:https://hub.docker.com/_/elasticsearch/[Docker]
-** link:https://www.elastic.co/guide/en/elasticsearch/reference/current/install-elasticsearch.html#elasticsearch-install-packages[Self-Managed Elasticsearch]
-** link:https://www.elastic.co/cloud/elasticsearch-service/signup?page=docs&placement=docs-body[Elastic Cloud]
+A running Elasticsearch instance. The following options are available:
 
-== Dependencies
+* link:https://hub.docker.com/_/elasticsearch/[Docker]
+* link:https://www.elastic.co/guide/en/elasticsearch/reference/current/install-elasticsearch.html#elasticsearch-install-packages[Self-Managed Elasticsearch]
+* link:https://www.elastic.co/cloud/elasticsearch-service/signup?page=docs&placement=docs-body[Elastic Cloud]
 
-Add the Elasticsearch Vector Store dependency to your project:
+
+== Auto-configuration
+
+Spring AI provides Spring Boot auto-configuration for the Elasticsearch Vector Store.
+To enable it, add the following dependency to your project's Maven `pom.xml` file:
 
 [source,xml]
 ----
 <dependency>
     <groupId>org.springframework.ai</groupId>
-    <artifactId>spring-ai-elasticsearch-store</artifactId>
+    <artifactId>spring-ai-elasticsearch-store-spring-boot-starter</artifactId>
 </dependency>
 ----
 
@@ -28,13 +31,62 @@ or to your Gradle `build.gradle` build file.
 [source,groovy]
 ----
 dependencies {
-    implementation 'org.springframework.ai:spring-ai-elasticsearch-store'
+    implementation 'org.springframework.ai:spring-ai-elasticsearch-store-spring-boot-starter'
 }
 ----
 
 TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.
 
-== Configuration
+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.
+
+Here is an example of the needed bean:
+
+[source,java]
+----
+@Bean
+public EmbeddingClient embeddingCLient() {
+    // Can be any other EmbeddingClient implementation
+    return new OpenAiEmbeddingClient(new OpenAiApi(System.getenv("SPRING_AI_OPENAI_API_KEY")));
+}
+----
+
+In cases where the Spring Boot auto-configured Elasticsearch `RestClient` bean is not what you want or need, you can still define your own bean.
+Please 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.
+
+[source,java]
+----
+@Bean
+public RestClient restClienbt() {
+    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();
+}
+----
+
+Now you can auto-wire the `ElasticsearchVectorStore` as a vector store in your application.
+
+[source,java]
+----
+@Autowired VectorStore vectorStore;
+
+// ...
+
+List <Document> documents = List.of(
+    new Document("Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!!", Map.of("meta1", "meta1")),
+    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
+vectorStore.add(documents);
+
+// Retrieve documents similar to a query
+List<Document> results = vectorStore.similaritySearch(SearchRequest.query("Spring").withTopK(5));
+----
+
+[[elasticsearchvector-properties]]
+=== Configuration Properties
 
 To connect to Elasticsearch and use the `ElasticsearchVectorStore`, you need to provide access details for your instance.
 A simple configuration can either be provided via Spring Boot's `application.yml`,
@@ -71,16 +123,44 @@ NOTE: If you choose to create a shell script for ease in future work, be sure to
 
 Spring Boot's auto-configuration feature for the Elasticsearch RestClient will create a bean instance that will be used by the `ElasticsearchVectorStore`.
 
-== Auto-configuration
+The Spring Boot properties starting with `spring.elasticsearch.*` are used to configure the Elasticsearch client:
 
-Spring AI provides Spring Boot auto-configuration for the Elasticsearch Vector Store.
-To enable it, add the following dependency to your project's Maven `pom.xml` file:
+|===
+|Property | Description | Default Value
+
+| spring.elasticsearch.connection-timeout | Connection timeout used when communicating with Elasticsearch. | 1s
+| spring.elasticsearch.password | Password for authentication with Elasticsearch. | -
+| spring.elasticsearch.username | Username for authentication with Elasticsearch.| -
+| spring.elasticsearch.path-prefix | Prefix added to the path of every request sent to Elasticsearch. | -
+| spring.elasticsearch.restclient.sniffer.delay-after-failure | Delay of a sniff execution scheduled after a failure.| 1m
+| spring.elasticsearch.restclient.sniffer.interval | Interval between consecutive ordinary sniff executions. | 5m
+| spring.elasticsearch.restclient.ssl.bundle | SSL bundle name. | -
+| spring.elasticsearch.socket-keep-alive | Whether to enable socket keep alive between client and Elasticsearch. | false
+| spring.elasticsearch.socket-timeout | Socket timeout used when communicating with Elasticsearch. | 30s
+| spring.elasticsearch.uris | Comma-separated list of the Elasticsearch instances to use. | http://localhost:9200
+|===
+
+
+The properties with `spring.ai.vectorstore.elasticsearch.*` prefix help to configure Elasticsearch vector store.
+
+|===
+|Property | Description | Default Value
+
+|`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`
+|===
+
+=== Manual Configuration
+
+Instead of using the Spring Boot auto-configuration, you can manually configure the Elasticsearch vector store. \For this you need to add the `spring-ai-elasticsearch-store` to your project:
 
 [source,xml]
 ----
 <dependency>
     <groupId>org.springframework.ai</groupId>
-    <artifactId>spring-ai-elasticsearch-store-spring-boot-starter</artifactId>
+    <artifactId>spring-ai-elasticsearch-store</artifactId>
 </dependency>
 ----
 
@@ -89,42 +169,10 @@ or to your Gradle `build.gradle` build file.
 [source,groovy]
 ----
 dependencies {
-    implementation 'org.springframework.ai:spring-ai-elasticsearch-store-spring-boot-starter'
-}
-----
-
-TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.
-
-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.
-
-Here is an example of the needed bean:
-
-[source,java]
-----
-@Bean
-public EmbeddingClient embeddingCLient() {
-    // Can be any other EmbeddingClient implementation
-    return new OpenAiEmbeddingClient(new OpenAiApi(System.getenv("SPRING_AI_OPENAI_API_KEY")));
-}
-----
-
-In cases where the Spring Boot auto-configured Elasticsearch `RestClient` bean is not what you want or need, you can still define your own bean.
-Please 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.
-
-[source,java]
-----
-@Bean
-public RestClient restClienbt() {
-    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();
+    implementation 'org.springframework.ai:spring-ai-elasticsearch-store'
 }
 ----
 
-Now you can auto-wire the `ElasticsearchVectorStore` as a vector store in your application.
-
 == Metadata Filtering
 
 You can leverage the generic, portable xref:api/vectordbs.adoc#metadata-filters[metadata filters] with Elasticsearcg as well.
@@ -171,15 +219,3 @@ is converted into the proprietary Elasticsearch filter format:
 (metadata.author:john OR jill) AND metadata.article_type:blog
 ----
 
-[[elasticsearchvector-properties]]
-== ElasticsearchVectorStore Properties
-
-You can use the following properties in your Spring Boot configuration to customize the Elasticsearch vector store.
-
-|===
-|Property |Default Value
-
-|`spring.ai.vectorstore.elasticsearch.index-name`
-|spring-ai-document-index
-|===
-

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

@@ -18,17 +18,18 @@
 import org.elasticsearch.client.RestClient;
 import org.springframework.ai.embedding.EmbeddingClient;
 import org.springframework.ai.vectorstore.ElasticsearchVectorStore;
+import org.springframework.ai.vectorstore.ElasticsearchVectorStoreOptions;
 import org.springframework.boot.autoconfigure.AutoConfiguration;
 import org.springframework.boot.autoconfigure.condition.ConditionalOnClass;
 import org.springframework.boot.autoconfigure.condition.ConditionalOnMissingBean;
-import org.springframework.boot.autoconfigure.elasticsearch.ElasticsearchClientAutoConfiguration;
 import org.springframework.boot.autoconfigure.elasticsearch.ElasticsearchRestClientAutoConfiguration;
 import org.springframework.boot.context.properties.EnableConfigurationProperties;
 import org.springframework.context.annotation.Bean;
 import org.springframework.util.StringUtils;
 
 /**
  * @author Eddú Meléndez
+ * @author Wei Jiang
  * @since 1.0.0
  */
 @AutoConfiguration(after = ElasticsearchRestClientAutoConfiguration.class)
@@ -40,10 +41,22 @@ class ElasticsearchVectorStoreAutoConfiguration {
 	@ConditionalOnMissingBean
 	ElasticsearchVectorStore vectorStore(ElasticsearchVectorStoreProperties properties, RestClient restClient,
 			EmbeddingClient embeddingClient) {
+		ElasticsearchVectorStoreOptions elasticsearchVectorStoreOptions = new ElasticsearchVectorStoreOptions();
+
 		if (StringUtils.hasText(properties.getIndexName())) {
-			return new ElasticsearchVectorStore(properties.getIndexName(), restClient, embeddingClient);
+			elasticsearchVectorStoreOptions.setIndexName(properties.getIndexName());
+		}
+		if (properties.getDimensions() != null) {
+			elasticsearchVectorStoreOptions.setDimensions(properties.getDimensions());
+		}
+		if (properties.isDenseVectorIndexing() != null) {
+			elasticsearchVectorStoreOptions.setDenseVectorIndexing(properties.isDenseVectorIndexing());
 		}
-		return new ElasticsearchVectorStore(restClient, embeddingClient);
+		if (StringUtils.hasText(properties.getSimilarity())) {
+			elasticsearchVectorStoreOptions.setSimilarity(properties.getSimilarity());
+		}
+
+		return new ElasticsearchVectorStore(elasticsearchVectorStoreOptions, restClient, embeddingClient);
 	}
 
 }

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

@@ -19,13 +19,32 @@
 
 /**
  * @author Eddú Meléndez
+ * @author Wei Jiang
  * @since 1.0.0
  */
 @ConfigurationProperties(prefix = "spring.ai.vectorstore.elasticsearch")
 public class ElasticsearchVectorStoreProperties {
 
+	/**
+	 * The name of the index to store the vectors.
+	 */
 	private String indexName;
 
+	/**
+	 * The number of dimensions in the vector.
+	 */
+	private Integer dimensions;
+
+	/**
+	 * Whether to use dense vector indexing.
+	 */
+	private Boolean denseVectorIndexing;
+
+	/**
+	 * The similarity function to use.
+	 */
+	private String similarity;
+
 	public String getIndexName() {
 		return this.indexName;
 	}
@@ -34,4 +53,28 @@ public void setIndexName(String indexName) {
 		this.indexName = indexName;
 	}
 
+	public Integer getDimensions() {
+		return dimensions;
+	}
+
+	public void setDimensions(Integer dimensions) {
+		this.dimensions = dimensions;
+	}
+
+	public Boolean isDenseVectorIndexing() {
+		return denseVectorIndexing;
+	}
+
+	public void setDenseVectorIndexing(Boolean denseVectorIndexing) {
+		this.denseVectorIndexing = denseVectorIndexing;
+	}
+
+	public String getSimilarity() {
+		return similarity;
+	}
+
+	public void setSimilarity(String similarity) {
+		this.similarity = similarity;
+	}
+
 }

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

@@ -16,6 +16,7 @@
 package org.springframework.ai.autoconfigure.vectorstore.elasticsearch;
 
 import org.awaitility.Awaitility;
+import org.junit.jupiter.api.Test;
 import org.junit.jupiter.api.condition.EnabledIfEnvironmentVariable;
 import org.junit.jupiter.params.ParameterizedTest;
 import org.junit.jupiter.params.provider.ValueSource;
@@ -107,6 +108,33 @@ public void addAndSearchTest(String similarityFunction) {
 		});
 	}
 
+	@Test
+	public void propertiesTest() {
+
+		new ApplicationContextRunner()
+			.withConfiguration(AutoConfigurations.of(ElasticsearchRestClientAutoConfiguration.class,
+					ElasticsearchVectorStoreAutoConfiguration.class, RestClientAutoConfiguration.class,
+					SpringAiRetryAutoConfiguration.class, OpenAiAutoConfiguration.class))
+			.withPropertyValues("spring.elasticsearch.uris=" + elasticsearchContainer.getHttpHostAddress(),
+					"spring.ai.openai.api-key=" + System.getenv("OPENAI_API_KEY"),
+					"spring.ai.vectorstore.elasticsearch.index-name=example",
+					"spring.ai.vectorstore.elasticsearch.dimensions=1024",
+					"spring.ai.vectorstore.elasticsearch.dense-vector-indexing=true",
+					"spring.ai.vectorstore.elasticsearch.similarity=dot_product")
+			.run(context -> {
+				var properties = context.getBean(ElasticsearchVectorStoreProperties.class);
+				var elasticsearchVectorStore = context.getBean(ElasticsearchVectorStore.class);
+
+				assertThat(properties).isNotNull();
+				assertThat(properties.getIndexName()).isEqualTo("example");
+				assertThat(properties.getDimensions()).isEqualTo(1024);
+				assertThat(properties.isDenseVectorIndexing()).isTrue();
+				assertThat(properties.getSimilarity()).isEqualTo("dot_product");
+
+				assertThat(elasticsearchVectorStore).isNotNull();
+			});
+	}
+
 	private String getText(String uri) {
 		var resource = new DefaultResourceLoader().getResource(uri);
 		try {

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

@@ -35,7 +35,6 @@
 		<dependency>
 			<groupId>co.elastic.clients</groupId>
 			<artifactId>elasticsearch-java</artifactId>
-			<version>8.12.2</version>
 		</dependency>
 
 		<!-- TESTING -->