Add documentation for Elasticsearch VectorStore.

Author: Vrryou

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

@@ -0,0 +1,144 @@
+= Elasticsearch
+
+This section walks you through setting up the Elasticsearch `VectorStore` to store document embeddings and perform similarity searches.
+
+link:https://www.elastic.co/elasticsearch[Elasticsearch] is an open source search and analytics engine based on the Apache Lucene library.
+
+== Prerequisites
+
+First you need access to an Elasticsearch instance.
+
+On startup, the `ElasticsearchVectorStore` will attempt to create the index mapping.
+
+Optionally, you can do this manually like so:
+
+[source,text]
+----
+PUT spring-ai-document-index
+{
+    "mappings": {
+        "properties": {
+            "embedding": {
+                "type": "dense_vector",
+                "dims": 1536,
+                "index": true,
+                "similarity": cosine
+            }
+        }
+    }
+}
+----
+
+TIP: replace the `1536` with the actual embedding dimension if you are using a different dimension.
+
+Next if required, an API key for the xref:api/embeddings.adoc#available-implementations[EmbeddingClient] to generate the embeddings stored by the `ElasticsearchVectorStore`.
+
+== Dependencies
+
+Add the ElasticsearchVectorStore boot starter dependency to your project:
+
+[source,xml]
+----
+<dependency>
+    <groupId>org.springframework.ai</groupId>
+    <artifactId>spring-ai-elasticsearch-store-spring-boot-starter</artifactId>
+</dependency>
+----
+
+or to your Gradle `build.gradle` build file.
+
+[source,groovy]
+----
+dependencies {
+    implementation 'org.springframework.ai:spring-ai-elasticsearch-store-spring-boot-starter'
+}
+----
+
+The Vector Store, also requires an `EmbeddingClient` instance to calculate embeddings for the documents.
+You can pick one of the available xref:api/embeddings.adoc#available-implementations[EmbeddingClient Implementations].
+
+For example to use the xref:api/embeddings/openai-embeddings.adoc[OpenAI EmbeddingClient] add the following dependency to your project:
+
+[source,xml]
+----
+<dependency>
+	<groupId>org.springframework.ai</groupId>
+	<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
+</dependency>
+----
+
+or to your Gradle `build.gradle` build file.
+
+[source,groovy]
+----
+dependencies {
+    implementation 'org.springframework.ai:spring-ai-openai-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.
+Refer to the xref:getting-started.adoc#repositories[Repositories] section to add Milestone and/or Snapshot Repositories to your build file.
+
+To connect to Elasticsearch, create a `RestClient` bean and provide the access details for your instance.
+
+[source,java]
+----
+@Bean
+public RestClient restClient() {
+    return RestClient.builder(HttpHost.create("http://localhost:9200"))
+            .build();
+}
+----
+
+Next, you can provide the `ElasticsearchVectorStore` configuration via Spring Boot's `application.yml`:
+
+[source,yaml]
+----
+spring:
+  ai:
+    vectorstore:
+      elasticsearch:
+        index-name: spring-ai-index
+----
+
+TIP: Check the list of <<elasticsearchvector-properties, configuration parameters>> to learn about the default values and configuration options.
+
+In your main code, create some documents:
+
+[source,java]
+----
+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 Elasticsearch:
+
+[source,java]
+----
+vectorStore.add(List.of(document));
+----
+
+And finally, retrieve documents similar to a query:
+
+[source,java]
+----
+List<Document> results = vectorStore.similaritySearch(SearchRequest.query("Spring").withTopK(5));
+----
+
+If all goes well, you should retrieve the document containing the text "Spring AI rocks!!".
+
+[[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
+|===
+