Allow configuration of vector index name.

At the moment, it is not possible to configure SpringAI
to use an existing index in the database.
This commit enables the user to provide the index name
for auto configuration or builder usage.

Author: meistermeier

spring-ai-spring-boot-autoconfigure/src/main/java/org/springframework/ai/autoconfigure/vectorstore/neo4j/Neo4jVectorStoreAutoConfiguration.java

@@ -56,6 +56,7 @@ public VectorStore vectorStore(Driver driver, EmbeddingClient embeddingClient,
 			.withDistanceType(properties.getDistanceType())
 			.withLabel(properties.getLabel())
 			.withEmbeddingProperty(properties.getEmbeddingProperty())
+			.withIndexName(properties.getIndexName())
 			.build();
 
 		return new Neo4jVectorStore(driver, embeddingClient, config);

spring-ai-spring-boot-autoconfigure/src/main/java/org/springframework/ai/autoconfigure/vectorstore/neo4j/Neo4jVectorStoreProperties.java

@@ -37,6 +37,8 @@ public class Neo4jVectorStoreProperties {
 
 	private String embeddingProperty = Neo4jVectorStore.DEFAULT_EMBEDDING_PROPERTY;
 
+	private String indexName = Neo4jVectorStore.DEFAULT_INDEX_NAME;
+
 	public String getDatabaseName() {
 		return databaseName;
 	}
@@ -77,4 +79,12 @@ public void setEmbeddingProperty(String embeddingProperty) {
 		this.embeddingProperty = embeddingProperty;
 	}
 
+	public String getIndexName() {
+		return this.indexName;
+	}
+
+	public void setIndexName(String indexName) {
+		this.indexName = indexName;
+	}
+
 }

spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/vectorstore/neo4j/Neo4jVectorStoreAutoConfigurationIT.java

@@ -66,11 +66,13 @@ public class Neo4jVectorStoreAutoConfigurationIT {
 	void addAndSearch() {
 		contextRunner
 			.withPropertyValues("spring.ai.vectorstore.neo4j.label=my_test_label",
-					"spring.ai.vectorstore.neo4j.embeddingDimension=384")
+					"spring.ai.vectorstore.neo4j.embeddingDimension=384",
+					"spring.ai.vectorstore.neo4j.indexName=customIndexName")
 			.run(context -> {
 				var properties = context.getBean(Neo4jVectorStoreProperties.class);
 				assertThat(properties.getLabel()).isEqualTo("my_test_label");
 				assertThat(properties.getEmbeddingDimension()).isEqualTo(384);
+				assertThat(properties.getIndexName()).isEqualTo("customIndexName");
 
 				VectorStore vectorStore = context.getBean(VectorStore.class);
 				vectorStore.add(documents);

vector-stores/spring-ai-neo4j-store/src/main/java/org/springframework/ai/vectorstore/Neo4jVectorStore.java

@@ -69,6 +69,8 @@ public static final class Neo4jVectorStoreConfig {
 
 		private final String quotedLabel;
 
+		private final String indexName;
+
 		/**
 		 * Start building a new configuration.
 		 * @return The entry point for creating a new configuration.
@@ -97,6 +99,7 @@ private Neo4jVectorStoreConfig(Builder builder) {
 			this.label = builder.label;
 			this.embeddingProperty = builder.embeddingProperty;
 			this.quotedLabel = SchemaNames.sanitize(this.label).orElseThrow();
+			this.indexName = builder.indexName;
 		}
 
 		public static class Builder {
@@ -111,6 +114,8 @@ public static class Builder {
 
 			private String embeddingProperty = DEFAULT_EMBEDDING_PROPERTY;
 
+			private String indexName = DEFAULT_INDEX_NAME;
+
 			private Builder() {
 			}
 
@@ -182,6 +187,21 @@ public Builder withEmbeddingProperty(String newEmbeddingProperty) {
 				return this;
 			}
 
+			/**
+			 * Configures the vector index to be used. Defaults to
+			 * {@literal spring-ai-document-index}.
+			 * @param newIndexName The name of the index to be used for storing and
+			 * searching data.
+			 * @return this builder
+			 */
+			public Builder withIndexName(String newIndexName) {
+
+				Assert.hasText(newIndexName, "Index name may not be null or blank");
+
+				this.indexName = newIndexName;
+				return this;
+			}
+
 			/**
 			 * {@return the immutable configuration}
 			 */
@@ -198,7 +218,7 @@ public Neo4jVectorStoreConfig build() {
 
 	public static final String DEFAULT_LABEL = "Document";
 
-	private static final String INDEX_NAME = "spring-ai-document-index";
+	public static final String DEFAULT_INDEX_NAME = "spring-ai-document-index";
 
 	public static final String DEFAULT_EMBEDDING_PROPERTY = "embedding";
 
@@ -275,7 +295,7 @@ public List<Document> similaritySearch(SearchRequest request) {
 						WHERE score >= $threshold
 						RETURN node, score
 						""",
-						Map.of("indexName", INDEX_NAME, "numberOfNearestNeighbours", request.getTopK(),
+						Map.of("indexName", this.config.indexName, "numberOfNearestNeighbours", request.getTopK(),
 								"embeddingValue", embedding, "threshold", request.getSimilarityThreshold()))
 				.list(Neo4jVectorStore::recordToDocument);
 		}
@@ -292,15 +312,16 @@ public void afterPropertiesSet() {
 				.consume();
 
 			var vectorIndexExists = session
-				.run("SHOW INDEXES YIELD name WHERE name = $name RETURN count(*) > 0", Map.of("name", INDEX_NAME))
+				.run("SHOW INDEXES YIELD name WHERE name = $name RETURN count(*) > 0",
+						Map.of("name", this.config.indexName))
 				.single()
 				.get(0)
 				.asBoolean();
 
 			if (!vectorIndexExists) {
 				var statement = "CALL db.index.vector.createNodeIndex($indexName, $label, $embeddingProperty, $embeddingDimension, $distanceType)";
 				session.run(statement,
-						Map.of("indexName", INDEX_NAME, "label", this.config.label, "embeddingProperty",
+						Map.of("indexName", this.config.indexName, "label", this.config.label, "embeddingProperty",
 								this.config.embeddingProperty, "embeddingDimension", this.config.embeddingDimension,
 								"distanceType", this.config.distanceType.name))
 					.consume();
@@ -323,7 +344,7 @@ private Map<String, Object> documentToRecord(Document document) {
 		document.getMetadata().forEach((k, v) -> properties.put("metadata." + k, Values.value(v)));
 		row.put("properties", properties);
 
-		row.put(DEFAULT_EMBEDDING_PROPERTY, Values.value(toFloatArray(embedding)));
+		row.put(this.config.embeddingProperty, Values.value(toFloatArray(embedding)));
 		return row;
 	}