Standardize builder class names in vector stores

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

@@ -163,9 +163,7 @@ For more complex scenarios, the builder pattern offers extensive configuration o
 ----
 @Bean
 public VectorStore vectorStore(CqlSession session, EmbeddingModel embeddingModel) {
-    return CassandraVectorStore.builder()
-        .session(session)
-        .embeddingModel(embeddingModel)
+    return CassandraVectorStore.builder(embeddingModel)
         .keyspace("my_keyspace")
         .table("my_vectors")
         .partitionKeys(List.of(new SchemaColumn("id", DataTypes.TEXT)))

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/azure-cosmos-db.adoc

@@ -203,11 +203,6 @@ public class DemoApplication implements CommandLineRunner {
 
     @Bean
     public VectorStore vectorStore(ObservationRegistry observationRegistry) {
-        CosmosDBVectorStoreConfig config = new CosmosDBVectorStoreConfig();
-        config.setDatabaseName("spring-ai-sample");
-        config.setContainerName("container");
-        config.setMetadataFields("country,city");
-        config.setVectorStoreThroughput(400);
 
         CosmosAsyncClient cosmosClient = new CosmosClientBuilder()
                 .endpoint(System.getenv("COSMOSDB_AI_ENDPOINT"))
@@ -216,7 +211,13 @@ public class DemoApplication implements CommandLineRunner {
                 .gatewayMode()
                 .buildAsyncClient();
 
-        return new CosmosDBVectorStore(observationRegistry, null, cosmosClient, config, this.embeddingModel);
+		return CosmosDBVectorStore.builder(cosmosClient, this.embeddingModel)
+				.databaseName("test-database")
+				.containerName("test-container")
+				.metadataFields(List.of("country", "year", "city"))
+				.vectorStoreThroughput(1000)
+                .observationRegistry(observationRegistry)
+				.build();
     }
 
     @Bean

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

@@ -131,12 +131,14 @@ To create a vector store, you can use the following code by injecting the `Searc
 ----
 @Bean
 public VectorStore vectorStore(SearchIndexClient searchIndexClient, EmbeddingModel embeddingModel) {
-  return new AzureVectorStore(searchIndexClient, embeddingModel,
+
+  return AzureVectorStore.builder(searchIndexClient, embeddingModel)
+    .initializeSchema(true)
     // Define the metadata fields to be used
     // in the similarity search filters.
-    List.of(MetadataField.text("country"),
-            MetadataField.int64("year"),
-            MetadataField.bool("active")));
+    .filterMetadataFields(List.of(MetadataField.text("country"), MetadataField.int64("year"),
+            MetadataField.date("activationDate")))
+    .build();
 }
 ----
 

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

@@ -231,7 +231,10 @@ Integrate with OpenAI's embeddings by adding the Spring Boot OpenAI starter to y
 ----
 @Bean
 public VectorStore chromaVectorStore(EmbeddingModel embeddingModel, ChromaApi chromaApi) {
- return new ChromaVectorStore(embeddingModel, chromaApi, "TestCollection", false);
+ return ChromaVectorStore.builder(chromaApi, embeddingModel)
+				.collectionName("TestCollection")
+				.initializeSchema(true)
+				.build();
 }
 ----
 

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

@@ -256,9 +256,7 @@ public VectorStore vectorStore(RestClient restClient, EmbeddingModel embeddingMo
     options.setSimilarity(COSINE);           // Optional: defaults to COSINE
     options.setDimensions(1536);             // Optional: defaults to model dimensions or 1536
 
-    return ElasticsearchVectorStore.builder()
-        .restClient(restClient)
-        .embeddingModel(embeddingModel)
+    return ElasticsearchVectorStore.builder(restClient, embeddingModel)
         .options(options)                     // Optional: use custom options
         .initializeSchema(true)               // Optional: defaults to false
         .batchingStrategy(new TokenCountBatchingStrategy()) // Optional: defaults to TokenCountBatchingStrategy

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

@@ -83,10 +83,13 @@ Here is a sample that creates an instance of the `GemfireVectorStore` instead of
 [source,java]
 ----
 @Bean
-public VectorStore vectorStore(EmbeddingModel embeddingModel) {
-    return new GemFireVectorStore(new GemFireVectorStoreConfig()
-                                    .setIndexName("my-vector-index")
-                                    .setPort(7071), embeddingClient);
+public GemFireVectorStore vectorStore(EmbeddingModel embeddingModel) {
+    return GemFireVectorStore.builder(embeddingModel)
+        .host("localhost")
+        .port(7071)
+        .indexName("my-vector-index")
+        .initializeSchema(true)
+        .build();
 }
 ----
 

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

@@ -149,8 +149,7 @@ Then create the `MariaDBVectorStore` bean using the builder pattern:
 ----
 @Bean
 public VectorStore vectorStore(JdbcTemplate jdbcTemplate, EmbeddingModel embeddingModel) {
-    return MariaDBVectorStore.builder(jdbcTemplate)
-        .embeddingModel(embeddingModel)
+    return MariaDBVectorStore.builder(jdbcTemplate, embeddingModel)
         .dimensions(1536)                      // Optional: defaults to 1536
         .distanceType(MariaDBDistanceType.COSINE) // Optional: defaults to COSINE
         .schemaName("mydb")                    // Optional: defaults to null

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

@@ -109,13 +109,14 @@ To configure MilvusVectorStore in your application, you can use the following se
 ----
 	@Bean
 	public VectorStore vectorStore(MilvusServiceClient milvusClient, EmbeddingModel embeddingModel) {
-		MilvusVectorStoreConfig config = MilvusVectorStoreConfig.builder()
-			.withCollectionName("test_vector_store")
-			.withDatabaseName("default")
-			.withIndexType(IndexType.IVF_FLAT)
-			.withMetricType(MetricType.COSINE)
-			.build();
-		return new MilvusVectorStore(milvusClient, embeddingModel, config);
+		return MilvusVectorStore.builder(milvusClient, embeddingModel)
+				.collectionName("test_vector_store")
+				.databaseName("default")
+				.indexType(IndexType.IVF_FLAT)
+				.metricType(MetricType.COSINE)
+				.batchingStrategy(new TokenCountBatchingStrategy())
+				.initializeSchema(true)
+				.build();
 	}
 
 	@Bean

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

@@ -146,9 +146,7 @@ Then create the `MongoDBAtlasVectorStore` bean using the builder pattern:
 ----
 @Bean
 public VectorStore vectorStore(MongoTemplate mongoTemplate, EmbeddingModel embeddingModel) {
-    return MongoDBAtlasVectorStore.builder()
-        .mongoTemplate(mongoTemplate)
-        .embeddingModel(embeddingModel)
+    return MongoDBAtlasVectorStore.builder(mongoTemplate, embeddingModel)
         .collectionName("custom_vector_store")           // Optional: defaults to "vector_store"
         .vectorIndexName("custom_vector_index")          // Optional: defaults to "vector_index"
         .pathName("custom_embedding")                    // Optional: defaults to "embedding"

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/neo4j.adoc

@@ -172,9 +172,7 @@ Then create the `Neo4jVectorStore` bean using the builder pattern:
 ----
 @Bean
 public VectorStore vectorStore(Driver driver, EmbeddingModel embeddingModel) {
-    return Neo4jVectorStore.builder()
-        .driver(driver)
-        .embeddingModel(embeddingModel)
+    return Neo4jVectorStore.builder(driver, embeddingModel)
         .databaseName("neo4j")                // Optional: defaults to "neo4j"
         .distanceType(Neo4jDistanceType.COSINE) // Optional: defaults to COSINE
         .dimensions(1536)                      // Optional: defaults to 1536

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

@@ -177,9 +177,7 @@ Then create the `OpenSearchVectorStore` bean using the builder pattern:
 ----
 @Bean
 public VectorStore vectorStore(OpenSearchClient openSearchClient, EmbeddingModel embeddingModel) {
-    return OpenSearchVectorStore.builder()
-        .openSearchClient(openSearchClient)
-        .embeddingModel(embeddingModel)
+    return OpenSearchVectorStore.builder(openSearchClient, embeddingModel)
         .index("custom-index")                // Optional: defaults to "spring-ai-document-index"
         .similarityFunction("l2")             // Optional: defaults to "cosinesimil"
         .initializeSchema(true)               // Optional: defaults to false

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

@@ -220,9 +220,7 @@ To configure PgVector in your application, you can use the following setup:
 ----
 @Bean
 public VectorStore vectorStore(JdbcTemplate jdbcTemplate, EmbeddingModel embeddingModel) {
-    return PgVectorStore.builder()
-        .jdbcTemplate(jdbcTemplate)
-        .embeddingModel(embeddingModel)
+    return PgVectorStore.builder(jdbcTemplate, embeddingModel)
         .dimensions(1536)                    // Optional: defaults to model dimensions or 1536
         .distanceType(COSINE_DISTANCE)       // Optional: defaults to COSINE_DISTANCE
         .indexType(HNSW)                     // Optional: defaults to HNSW

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

@@ -188,30 +188,15 @@ To configure Pinecone in your application, you can use the following setup:
 [source,java]
 ----
 @Bean
-public PineconeVectorStoreConfig pineconeVectorStoreConfig() {
-
-    return PineconeVectorStoreConfig.builder()
-        .withApiKey(<PINECONE_API_KEY>)
-        .withEnvironment("gcp-starter")
-        .withProjectId("89309e6")
-        .withIndexName("spring-ai-test-index")
-        .withNamespace("") // the free tier doesn't support namespaces.
-        .withContentFieldName("my_content") // optional field to store the original content. Defaults to `document_content`
+public VectorStore pineconeVectorStore(EmbeddingModel embeddingModel) {
+    return PineconeVectorStore
+        .builder(embeddingModel, PINECONE_API_KEY, PINECONE_PROJECT_ID, PINECONE_ENVIRONMENT, PINECONE_INDEX_NAME)
+        .namespace(PINECONE_NAMESPACE) // the free tier doesn't support namespaces.
+        .contentFieldName(CUSTOM_CONTENT_FIELD_NAME) // optional field to store the original content. Defaults to `document_content`
         .build();
 }
 ----
 
-Integrate with OpenAI's embeddings by adding the Spring Boot OpenAI starter to your project.
-This provides you with an implementation of the Embeddings client:
-
-[source,java]
-----
-@Bean
-public VectorStore vectorStore(PineconeVectorStoreConfig config, EmbeddingModel embeddingModel) {
-    return new PineconeVectorStore(config, embeddingModel);
-}
-----
-
 In your main code, create some documents:
 
 [source,java]

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

@@ -148,8 +148,7 @@ Then create the `QdrantVectorStore` bean using the builder pattern:
 ----
 @Bean
 public VectorStore vectorStore(QdrantClient qdrantClient, EmbeddingModel embeddingModel) {
-    return QdrantVectorStore.builder(qdrantClient)
-        .embeddingModel(embeddingModel)
+    return QdrantVectorStore.builder(qdrantClient, embeddingModel)
         .collectionName("custom-collection")     // Optional: defaults to "vector_store"
         .initializeSchema(true)                  // Optional: defaults to false
         .batchingStrategy(new TokenCountBatchingStrategy()) // Optional: defaults to TokenCountBatchingStrategy

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

@@ -189,9 +189,7 @@ Then create the `RedisVectorStore` bean using the builder pattern:
 ----
 @Bean
 public VectorStore vectorStore(JedisPooled jedisPooled, EmbeddingModel embeddingModel) {
-    return RedisVectorStore.builder()
-        .jedis(jedisPooled)
-        .embeddingModel(embeddingModel)
+    return RedisVectorStore.builder(jedisPooled, embeddingModel)
         .indexName("custom-index")                // Optional: defaults to "spring-ai-index"
         .prefix("custom-prefix")                  // Optional: defaults to "embedding:"
         .metadataFields(                         // Optional: define metadata fields for filtering

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

@@ -161,9 +161,7 @@ Then create the `TypesenseVectorStore` bean using the builder pattern:
 ----
 @Bean
 public VectorStore vectorStore(Client client, EmbeddingModel embeddingModel) {
-    return TypesenseVectorStore.builder()
-        .client(client)
-        .embeddingModel(embeddingModel)
+    return TypesenseVectorStore.builder(client, embeddingModel)
         .collectionName("custom_vectors")     // Optional: defaults to "vector_store"
         .embeddingDimension(1536)            // Optional: defaults to 1536
         .initializeSchema(true)              // Optional: defaults to false

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

@@ -119,10 +119,8 @@ public WeaviateClient weaviateClient() {
 }
 
 @Bean
-public VectorStore vectorStore(EmbeddingModel embeddingModel, WeaviateClient weaviateClient) {
-    return WeaviateVectorStore.builder()
-        .weaviateClient(weaviateClient)
-        .embeddingModel(embeddingModel)
+public VectorStore vectorStore(WeaviateClient weaviateClient, EmbeddingModel embeddingModel) {
+    return WeaviateVectorStore.builder(weaviateClient, embeddingModel)
         .objectClass("CustomClass")                    // Optional: defaults to "SpringAiWeaviate"
         .consistencyLevel(ConsistentLevel.QUORUM)      // Optional: defaults to ConsistentLevel.ONE
         .filterMetadataFields(List.of(                 // Optional: fields that can be used in filters

spring-ai-spring-boot-autoconfigure/src/main/java/org/springframework/ai/autoconfigure/vectorstore/mongo/MongoDBAtlasVectorStoreAutoConfiguration.java

@@ -66,7 +66,7 @@ MongoDBAtlasVectorStore vectorStore(MongoTemplate mongoTemplate, EmbeddingModel
 			ObjectProvider<VectorStoreObservationConvention> customObservationConvention,
 			BatchingStrategy batchingStrategy) {
 
-		MongoDBAtlasVectorStore.MongoDBBuilder builder = MongoDBAtlasVectorStore.builder(mongoTemplate, embeddingModel)
+		MongoDBAtlasVectorStore.Builder builder = MongoDBAtlasVectorStore.builder(mongoTemplate, embeddingModel)
 			.initializeSchema(properties.isInitializeSchema())
 			.observationRegistry(observationRegistry.getIfUnique(() -> ObservationRegistry.NOOP))
 			.customObservationConvention(customObservationConvention.getIfAvailable(() -> null))

vector-stores/spring-ai-azure-cosmos-db-store/src/main/java/org/springframework/ai/vectorstore/cosmosdb/CosmosDBVectorStore.java

@@ -151,7 +151,7 @@ public CosmosDBVectorStore(ObservationRegistry observationRegistry,
 	 * create new CosmosDBVectorStore instances.
 	 * @param builder the configured builder instance
 	 */
-	protected CosmosDBVectorStore(CosmosDBBuilder builder) {
+	protected CosmosDBVectorStore(Builder builder) {
 		super(builder);
 
 		Assert.notNull(builder.cosmosClient, "CosmosClient must not be null");
@@ -172,8 +172,8 @@ protected CosmosDBVectorStore(CosmosDBBuilder builder) {
 		initializeContainer(containerName, databaseName, vectorStoreThroughput, vectorDimensions, partitionKeyPath);
 	}
 
-	public static CosmosDBBuilder builder(CosmosAsyncClient cosmosClient, EmbeddingModel embeddingModel) {
-		return new CosmosDBBuilder(cosmosClient, embeddingModel);
+	public static Builder builder(CosmosAsyncClient cosmosClient, EmbeddingModel embeddingModel) {
+		return new Builder(cosmosClient, embeddingModel);
 	}
 
 	private void initializeContainer(String containerName, String databaseName, int vectorStoreThroughput,
@@ -429,7 +429,7 @@ public VectorStoreObservationContext.Builder createObservationContextBuilder(Str
 	 *
 	 * @since 1.0.0
 	 */
-	public static class CosmosDBBuilder extends AbstractVectorStoreBuilder<CosmosDBBuilder> {
+	public static class Builder extends AbstractVectorStoreBuilder<Builder> {
 
 		private final CosmosAsyncClient cosmosClient;
 
@@ -450,7 +450,7 @@ public static class CosmosDBBuilder extends AbstractVectorStoreBuilder<CosmosDBB
 
 		private BatchingStrategy batchingStrategy = new TokenCountBatchingStrategy();
 
-		private CosmosDBBuilder(CosmosAsyncClient cosmosClient, EmbeddingModel embeddingModel) {
+		private Builder(CosmosAsyncClient cosmosClient, EmbeddingModel embeddingModel) {
 			super(embeddingModel);
 			Assert.notNull(cosmosClient, "CosmosClient must not be null");
 			this.cosmosClient = cosmosClient;
@@ -462,7 +462,7 @@ private CosmosDBBuilder(CosmosAsyncClient cosmosClient, EmbeddingModel embedding
 		 * @return the builder instance
 		 * @throws IllegalArgumentException if containerName is null or empty
 		 */
-		public CosmosDBBuilder containerName(String containerName) {
+		public Builder containerName(String containerName) {
 			Assert.hasText(containerName, "Container name must not be empty");
 			this.containerName = containerName;
 			return this;
@@ -474,7 +474,7 @@ public CosmosDBBuilder containerName(String containerName) {
 		 * @return the builder instance
 		 * @throws IllegalArgumentException if databaseName is null or empty
 		 */
-		public CosmosDBBuilder databaseName(String databaseName) {
+		public Builder databaseName(String databaseName) {
 			Assert.hasText(databaseName, "Database name must not be empty");
 			this.databaseName = databaseName;
 			return this;
@@ -486,7 +486,7 @@ public CosmosDBBuilder databaseName(String databaseName) {
 		 * @return the builder instance
 		 * @throws IllegalArgumentException if partitionKeyPath is null or empty
 		 */
-		public CosmosDBBuilder partitionKeyPath(String partitionKeyPath) {
+		public Builder partitionKeyPath(String partitionKeyPath) {
 			Assert.hasText(partitionKeyPath, "Partition key path must not be empty");
 			this.partitionKeyPath = partitionKeyPath;
 			return this;
@@ -498,7 +498,7 @@ public CosmosDBBuilder partitionKeyPath(String partitionKeyPath) {
 		 * @return the builder instance
 		 * @throws IllegalArgumentException if vectorStoreThroughput is not positive
 		 */
-		public CosmosDBBuilder vectorStoreThroughput(int vectorStoreThroughput) {
+		public Builder vectorStoreThroughput(int vectorStoreThroughput) {
 			Assert.isTrue(vectorStoreThroughput > 0, "Vector store throughput must be positive");
 			this.vectorStoreThroughput = vectorStoreThroughput;
 			return this;
@@ -510,7 +510,7 @@ public CosmosDBBuilder vectorStoreThroughput(int vectorStoreThroughput) {
 		 * @return the builder instance
 		 * @throws IllegalArgumentException if vectorDimensions is not positive
 		 */
-		public CosmosDBBuilder vectorDimensions(long vectorDimensions) {
+		public Builder vectorDimensions(long vectorDimensions) {
 			Assert.isTrue(vectorDimensions > 0, "Vector dimensions must be positive");
 			this.vectorDimensions = vectorDimensions;
 			return this;
@@ -521,7 +521,7 @@ public CosmosDBBuilder vectorDimensions(long vectorDimensions) {
 		 * @param metadataFieldsList the list of metadata fields
 		 * @return the builder instance
 		 */
-		public CosmosDBBuilder metadataFields(List<String> metadataFieldsList) {
+		public Builder metadataFields(List<String> metadataFieldsList) {
 			this.metadataFieldsList = metadataFieldsList != null ? new ArrayList<>(metadataFieldsList)
 					: new ArrayList<>();
 			return this;
@@ -533,7 +533,7 @@ public CosmosDBBuilder metadataFields(List<String> metadataFieldsList) {
 		 * @return the builder instance
 		 * @throws IllegalArgumentException if batchingStrategy is null
 		 */
-		public CosmosDBBuilder batchingStrategy(BatchingStrategy batchingStrategy) {
+		public Builder batchingStrategy(BatchingStrategy batchingStrategy) {
 			Assert.notNull(batchingStrategy, "BatchingStrategy must not be null");
 			this.batchingStrategy = batchingStrategy;
 			return this;