rebase

Author: l-trotta

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

@@ -48,6 +48,7 @@
 /**
  * @author Jemin Huh
  * @author Wei Jiang
+ * @author Laura Trotta
  * @since 1.0.0
  */
 public class ElasticsearchVectorStore implements VectorStore, InitializingBean {
@@ -62,8 +63,6 @@ public class ElasticsearchVectorStore implements VectorStore, InitializingBean {
 
 	private final FilterExpressionConverter filterExpressionConverter;
 
-	private String similarityFunction;
-
 	private final boolean initializeSchema;
 
 	public ElasticsearchVectorStore(RestClient restClient, EmbeddingModel embeddingModel, boolean initializeSchema) {
@@ -134,7 +133,7 @@ public List<Document> similaritySearch(SearchRequest searchRequest) {
 				threshold = 1 - threshold;
 			}
 			final float finalThreshold = threshold;
-			List<Float> vectors = this.embeddingClient.embed(searchRequest.getQuery())
+			List<Float> vectors = this.embeddingModel.embed(searchRequest.getQuery())
 				.stream()
 				.map(Double::floatValue)
 				.toList();
@@ -151,7 +150,6 @@ public List<Document> similaritySearch(SearchRequest searchRequest) {
 					Document.class);
 
 			return res.hits().hits().stream().map(this::toDocument).collect(Collectors.toList());
-
 		}
 		catch (IOException e) {
 			throw new RuntimeException(e);
@@ -219,4 +217,4 @@ public void afterPropertiesSet() {
 		}
 	}
 
-}
+}

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

@@ -1,10 +1,13 @@
 package org.springframework.ai.vectorstore;
 
-/*
-https://www.elastic.co/guide/en/elasticsearch/reference/master/dense-vector.html
-max_inner_product is currently not supported because the distance value is not
-normalized and would not comply with the requirement of being between 0 and 1
-*/
+/**
+ * https://www.elastic.co/guide/en/elasticsearch/reference/master/dense-vector.html
+ * max_inner_product is currently not supported because the distance value is not
+ * normalized and would not comply with the requirement of being between 0 and 1
+ *
+ * @author Laura Trotta
+ * @since 1.0.0
+ */
 public enum SimilarityFunction {
 
 	l2_norm, dot_product, cosine

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

@@ -64,7 +64,7 @@ class ElasticsearchVectorStoreIT {
 
 	@Container
 	private static final ElasticsearchContainer elasticsearchContainer = new ElasticsearchContainer(
-			"docker.elastic.co/elasticsearch/elasticsearch:8.12.2")
+			"docker.elastic.co/elasticsearch/elasticsearch:8.13.3")
 		.withEnv("xpack.security.enabled", "false");
 
 	private final List<Document> documents = List.of(
@@ -338,24 +338,24 @@ public void searchThresholdTest(String similarityFunction) {
 	public static class TestApplication {
 
 		@Bean("vectorStore_cosine")
-		public ElasticsearchVectorStore vectorStoreDefault(EmbeddingClient embeddingClient, RestClient restClient) {
-			return new ElasticsearchVectorStore(restClient, embeddingClient);
+		public ElasticsearchVectorStore vectorStoreDefault(EmbeddingModel embeddingModel, RestClient restClient) {
+			return new ElasticsearchVectorStore(restClient, embeddingModel, true);
 		}
 
 		@Bean("vectorStore_l2_norm")
-		public ElasticsearchVectorStore vectorStoreL2(EmbeddingClient embeddingClient, RestClient restClient) {
+		public ElasticsearchVectorStore vectorStoreL2(EmbeddingModel embeddingModel, RestClient restClient) {
 			ElasticsearchVectorStoreOptions options = new ElasticsearchVectorStoreOptions();
 			options.setIndexName("index_l2");
 			options.setSimilarity(SimilarityFunction.l2_norm);
-			return new ElasticsearchVectorStore(options, restClient, embeddingClient);
+			return new ElasticsearchVectorStore(options, restClient, embeddingModel,true);
 		}
 
 		@Bean("vectorStore_dot_product")
-		public ElasticsearchVectorStore vectorStoreDotProduct(EmbeddingClient embeddingClient, RestClient restClient) {
+		public ElasticsearchVectorStore vectorStoreDotProduct(EmbeddingModel embeddingModel, RestClient restClient) {
 			ElasticsearchVectorStoreOptions options = new ElasticsearchVectorStoreOptions();
 			options.setIndexName("index_dot_product");
 			options.setSimilarity(SimilarityFunction.dot_product);
-			return new ElasticsearchVectorStore(options, restClient, embeddingClient);
+			return new ElasticsearchVectorStore(options, restClient, embeddingModel,true);
 		}
 
 		@Bean