This version is still in development and is not considered stable yet. For the latest snapshot version, please use Spring AI 1.0.0-SNAPSHOT!

MariaDB Vector Store

This section walks you through setting up MariaDBVectorStore to store document embeddings and perform similarity searches.

MariaDB Vector is part of MariaDB 11.7 and enables storing and searching over machine learning-generated embeddings. It provides efficient vector similarity search capabilities using vector indexes, supporting both cosine similarity and Euclidean distance metrics.

Prerequisites

Auto-Configuration

Spring AI provides Spring Boot auto-configuration for the MariaDB Vector Store. To enable it, add the following dependency to your project’s Maven pom.xml file:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-mariadb-store-spring-boot-starter</artifactId>
</dependency>

or to your Gradle build.gradle build file.

dependencies {
    implementation 'org.springframework.ai:spring-ai-mariadb-store-spring-boot-starter'
}
Refer to the Dependency Management section to add the Spring AI BOM to your build file.

The vector store implementation can initialize the required schema for you, but you must opt-in by specifying the initializeSchema boolean in the appropriate constructor or by setting …​initialize-schema=true in the application.properties file.

This is a breaking change! In earlier versions of Spring AI, this schema initialization happened by default.

Additionally, you will need a configured EmbeddingModel bean. Refer to the EmbeddingModel section for more information.

For example, to use the OpenAI EmbeddingModel, add the following dependency:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>
Refer to the Repositories section to add Milestone and/or Snapshot Repositories to your build file.

Now you can auto-wire the MariaDBVectorStore in your application:

@Autowired VectorStore vectorStore;

// ...

List<Document> documents = List.of(
    new Document("Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!!", Map.of("meta1", "meta1")),
    new Document("The World is Big and Salvation Lurks Around the Corner"),
    new Document("You walk forward facing the past and you turn back toward the future.", Map.of("meta2", "meta2")));

// Add the documents to MariaDB
vectorStore.add(documents);

// Retrieve documents similar to a query
List<Document> results = vectorStore.similaritySearch(SearchRequest.builder().query("Spring").topK(5).build());

Configuration Properties

To connect to MariaDB and use the MariaDBVectorStore, you need to provide access details for your instance. A simple configuration can be provided via Spring Boot’s application.yml:

spring:
  datasource:
    url: jdbc:mariadb://localhost/db
    username: myUser
    password: myPassword
  ai:
    vectorstore:
      mariadb:
        initialize-schema: true
        distance-type: COSINE
        dimensions: 1536
If you run MariaDB Vector as a Spring Boot dev service via Docker Compose or Testcontainers, you don’t need to configure URL, username and password since they are autoconfigured by Spring Boot.

Properties starting with spring.ai.vectorstore.mariadb.* are used to configure the MariaDBVectorStore:

Property Description Default Value

spring.ai.vectorstore.mariadb.initialize-schema

Whether to initialize the required schema

false

spring.ai.vectorstore.mariadb.distance-type

Search distance type. Use COSINE (default) or EUCLIDEAN. If vectors are normalized to length 1, you can use EUCLIDEAN for best performance.

COSINE

spring.ai.vectorstore.mariadb.dimensions

Embeddings dimension. If not specified explicitly, will retrieve dimensions from the provided EmbeddingModel.

1536

spring.ai.vectorstore.mariadb.remove-existing-vector-store-table

Deletes the existing vector store table on startup.

false

spring.ai.vectorstore.mariadb.schema-name

Vector store schema name

null

spring.ai.vectorstore.mariadb.table-name

Vector store table name

vector_store

spring.ai.vectorstore.mariadb.schema-validation

Enables schema and table name validation to ensure they are valid and existing objects.

false
If you configure a custom schema and/or table name, consider enabling schema validation by setting spring.ai.vectorstore.mariadb.schema-validation=true. This ensures the correctness of the names and reduces the risk of SQL injection attacks.

Manual Configuration

Instead of using the Spring Boot auto-configuration, you can manually configure the MariaDB vector store. For this you need to add the following dependencies to your project:

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-jdbc</artifactId>
</dependency>

<dependency>
    <groupId>org.mariadb.jdbc</groupId>
    <artifactId>mariadb-java-client</artifactId>
    <scope>runtime</scope>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-mariadb-store</artifactId>
</dependency>
Refer to the Dependency Management section to add the Spring AI BOM to your build file.

Then create the MariaDBVectorStore bean using the builder pattern:

@Bean
public VectorStore vectorStore(JdbcTemplate 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
        .vectorTableName("custom_vectors")     // Optional: defaults to "vector_store"
        .contentFieldName("text")             // Optional: defaults to "content"
        .embeddingFieldName("embedding")      // Optional: defaults to "embedding"
        .idFieldName("doc_id")                // Optional: defaults to "id"
        .metadataFieldName("meta")           // Optional: defaults to "metadata"
        .initializeSchema(true)               // Optional: defaults to false
        .schemaValidation(true)              // Optional: defaults to false
        .removeExistingVectorStoreTable(false) // Optional: defaults to false
        .maxDocumentBatchSize(10000)         // Optional: defaults to 10000
        .build();
}

// This can be any EmbeddingModel implementation
@Bean
public EmbeddingModel embeddingModel() {
    return new OpenAiEmbeddingModel(new OpenAiApi(System.getenv("OPENAI_API_KEY")));
}

Metadata Filtering

You can leverage the generic, portable metadata filters with MariaDB Vector store.

For example, you can use either the text expression language:

vectorStore.similaritySearch(
    SearchRequest.builder()
        .query("The World")
        .topK(TOP_K)
        .similarityThreshold(SIMILARITY_THRESHOLD)
        .filterExpression("author in ['john', 'jill'] && article_type == 'blog'").build());

or programmatically using the Filter.Expression DSL:

FilterExpressionBuilder b = new FilterExpressionBuilder();

vectorStore.similaritySearch(SearchRequest.builder()
    .query("The World")
    .topK(TOP_K)
    .similarityThreshold(SIMILARITY_THRESHOLD)
    .filterExpression(b.and(
        b.in("author", "john", "jill"),
        b.eq("article_type", "blog")).build()).build());
These filter expressions are automatically converted into the equivalent MariaDB JSON path expressions.