Update vector store builder examples

sobychacko · leijendary · commit c315868de299 · 2025-01-24T01:11:17.000+01:00
Update documentation examples to show correct builder usage across all vector store implementations.
Demonstrate passing required parameters directly in builder() method.

Signed-off-by: leijendary &lt;jonathanleijendekker@gmail.com&gt;
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/apache-cassandra.adoc b/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)))
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/azure-cosmos-db.adoc b/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
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/azure.adoc b/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();
 }
 ----
 
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/chroma.adoc b/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();
 }
 ----
 
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/elasticsearch.adoc b/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
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/gemfire.adoc b/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();
 }
 ----
 
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/mariadb.adoc b/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
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/milvus.adoc b/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
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/mongodb.adoc b/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"
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/neo4j.adoc b/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
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/opensearch.adoc b/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
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pgvector.adoc b/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
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pinecone.adoc b/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]
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/qdrant.adoc b/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
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/redis.adoc b/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
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/typesense.adoc b/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
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/weaviate.adoc b/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
