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Update vector store builder examples

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Update documentation examples to show correct builder usage across all vector store implementations.
Demonstrate passing required parameters directly in builder() method.
This commit is contained in:
Soby Chacko
2024-12-20 18:06:56 -05:00
committed by Mark Pollack
parent f9d741dd85
commit d697e58c05
17 changed files with 49 additions and 74 deletions
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4
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/apache-cassandra.adoc
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@@ -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)))

13
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/azure-cosmos-db.adoc
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@@ -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

10
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/azure.adoc
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@@ -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();
}
----

5
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/chroma.adoc
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@@ -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();
}
----

4
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/elasticsearch.adoc
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@@ -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

11
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/gemfire.adoc
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@@ -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();
}
----

3
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/mariadb.adoc
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@@ -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

15
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/milvus.adoc
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@@ -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

4
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/mongodb.adoc
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@@ -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"

4
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/neo4j.adoc
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@@ -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

4
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/opensearch.adoc
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@@ -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

4
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pgvector.adoc
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@@ -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

25
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pinecone.adoc
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@@ -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]

3
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/qdrant.adoc
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@@ -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

4
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/redis.adoc
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@@ -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

4
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/typesense.adoc
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@@ -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

6
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/weaviate.adoc
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@@ -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