feat(vector-store) Add MariaDB Vector Store support to Spring AI

- Updated `org.springframework.boot.autoconfigure.AutoConfiguration.imports` to include MariaDB vector store auto-configuration
- Created MariaDB Vector Store autoconfiguration integration tests (`MariaDbStoreAutoConfigurationIT`)
- Added MariaDB store properties configuration and tests (`MariaDbStorePropertiesTests`)
- Introduced new Maven modules:
  - `spring-ai-mariadb-store`: Core MariaDB vector store implementation
  - `spring-ai-starter-mariadb-store`: Spring Boot starter for MariaDB vector store
- Added `MariaDBFilterExpressionConverter` to support JSON-based metadata filtering in MariaDB
- Implemented filter expression conversion for MariaDB vector store queries
- Added README.md with documentation link for MariaDB Vector Store
- Updated project dependencies to include MariaDB JDBC driver and test containers
- Configured integration testing with TestContainers for MariaDB
- Added observability support for MariaDB vector store operations

Author: rusher

README.md

@@ -25,7 +25,7 @@ You can find more details in the [Reference Documentation](https://docs.spring.i
   - [Moderation](https://docs.spring.io/spring-ai/reference/api/index.html#api/moderation)
 * Portable API support across AI providers for both synchronous and streaming API options are supported. Access to [model-specific features](https://docs.spring.io/spring-ai/reference/api/chatmodel.html#_chat_options) is also available.
 * [Structured Outputs](https://docs.spring.io/spring-ai/reference/api/structured-output-converter.html) - Mapping of AI Model output to POJOs.
-* Support for all major [Vector Database providers](https://docs.spring.io/spring-ai/reference/api/vectordbs.html) such as *Apache Cassandra, Azure Vector Search, Chroma, Milvus, MongoDB Atlas, Neo4j, Oracle, PostgreSQL/PGVector, PineCone, Qdrant, Redis, and Weaviate*.
+* Support for all major [Vector Database providers](https://docs.spring.io/spring-ai/reference/api/vectordbs.html) such as *Apache Cassandra, Azure Vector Search, Chroma, Milvus, MongoDB Atlas, MariaDB, Neo4j, Oracle, PostgreSQL/PGVector, PineCone, Qdrant, Redis, and Weaviate*.
 * Portable API across Vector Store providers, including a novel SQL-like [metadata filter API](https://docs.spring.io/spring-ai/reference/api/vectordbs.html#metadata-filters).
 * [Tools/Function Calling](https://docs.spring.io/spring-ai/reference/api/functions.html) - permits the model to request the execution of client-side tools and functions, thereby accessing necessary real-time information as required.
 * [Observability](https://docs.spring.io/spring-ai/reference/observability/index.html) - Provides insights into AI-related operations.

pom.xml

@@ -52,6 +52,7 @@
 		<module>vector-stores/spring-ai-elasticsearch-store</module>
 		<module>vector-stores/spring-ai-gemfire-store</module>
 		<module>vector-stores/spring-ai-hanadb-store</module>
+		<module>vector-stores/spring-ai-mariadb-store</module>
 		<module>vector-stores/spring-ai-milvus-store</module>
 		<module>vector-stores/spring-ai-mongodb-atlas-store</module>
 		<module>vector-stores/spring-ai-neo4j-store</module>
@@ -73,6 +74,7 @@
 		<module>spring-ai-spring-boot-starters/spring-ai-starter-elasticsearch-store</module>
 		<module>spring-ai-spring-boot-starters/spring-ai-starter-gemfire-store</module>
 		<module>spring-ai-spring-boot-starters/spring-ai-starter-hanadb-store</module>
+		<module>spring-ai-spring-boot-starters/spring-ai-starter-mariadb-store</module>
 		<module>spring-ai-spring-boot-starters/spring-ai-starter-milvus-store</module>
 		<module>spring-ai-spring-boot-starters/spring-ai-starter-mongodb-atlas-store</module>
 		<module>spring-ai-spring-boot-starters/spring-ai-starter-neo4j-store</module>
@@ -212,6 +214,7 @@
 		<qdrant.version>1.9.1</qdrant.version>
 		<typesense.version>0.5.0</typesense.version>
 		<opensearch-client.version>2.10.1</opensearch-client.version>
+		<mariadb.version>3.5.1</mariadb.version>
 		<commonmark.version>0.22.0</commonmark.version>
 
 		<!-- also managed by boot bom -->
@@ -678,28 +681,28 @@
 								<exclude>org.springframework.ai.vectorstore**/Hana**IT.java</exclude>
 								<exclude>org.springframework.ai.vectorstore**/Hana**IT.java</exclude>
 								<exclude>org.springframework.ai.vectorstore**/Milvus**IT.java</exclude>
-								<exclude>org.springframework.ai.vectorstore**/Mongo**IT.java</exclude>
+								<exclude>org.springframework.ai.vectorstore**/MariaDB**IT.java</exclude>
 								<exclude>org.springframework.ai.vectorstore**/Mongo**IT.java</exclude>
 								<exclude>org.springframework.ai.vectorstore**/Neo4j**IT.java</exclude>
 								<exclude>org.springframework.ai.vectorstore**/OpenSearch**IT.java</exclude>
 								<exclude>org.springframework.ai.vectorstore**/Oracle**IT.java</exclude>
 								<!-- <exclude>org.springframework.ai.vectorstore**/PgVector**IT.java</exclude> -->
-								<exclude>org.springframework.ai.vectorstore**/Pinecone**IT.java</exclude>								
+								<exclude>org.springframework.ai.vectorstore**/Pinecone**IT.java</exclude>
 								<exclude>org.springframework.ai.vectorstore.qdrant/**/**IT.java</exclude>
 								<exclude>org.springframework.ai.vectorstore**/Qdrant**IT.java</exclude>
 								<exclude>org.springframework.ai.vectorstore**/Redis**IT.java</exclude>
 								<exclude>org.springframework.ai.vectorstore**/Typesense**IT.java</exclude>
 								<exclude>org.springframework.ai.vectorstore**/Weaviate**IT.java</exclude>
 
 								<!-- Auto-configurations-->
-								 
+
 								 <!-- <exclude>org.springframework.ai.autoconfigure/**/**IT.java</exclude> -->
 
 								 <exclude>org.springframework.ai.autoconfigure.anthropic/**/**IT.java</exclude>
 								 <exclude>org.springframework.ai.autoconfigure.azure/**/**IT.java</exclude>
 								 <exclude>org.springframework.ai.autoconfigure.bedrock/**/**IT.java</exclude>
 								 <exclude>org.springframework.ai.autoconfigure.huggingface/**/**IT.java</exclude>
-								 
+
 								 <exclude>org.springframework.ai.autoconfigure.chat/**/**IT.java</exclude>
 								 <exclude>org.springframework.ai.autoconfigure.embedding/**/**IT.java</exclude>
 								 <exclude>org.springframework.ai.autoconfigure.image/**/**IT.java</exclude>
@@ -713,24 +716,24 @@
 								 <!-- <exclude>org.springframework.ai.autoconfigure.openai/**/**IT.java</exclude> -->
 								 <exclude>org.springframework.ai.autoconfigure.postgresml/**/**IT.java</exclude>
 								 <exclude>org.springframework.ai.autoconfigure.qianfan/**/**IT.java</exclude>
-								 
+
 								 <exclude>org.springframework.ai.autoconfigure.retry/**/**IT.java</exclude>
-								 
+
 								 <exclude>org.springframework.ai.autoconfigure.stabilityai/**/**IT.java</exclude>
 								 <exclude>org.springframework.ai.autoconfigure.transformers/**/**IT.java</exclude>
-								 
-								 <exclude>org.springframework.ai.autoconfigure.vectorstore/**/**IT.java</exclude>								 
-								 
+
+								 <exclude>org.springframework.ai.autoconfigure.vectorstore/**/**IT.java</exclude>
+
 								 <exclude>org.springframework.ai.autoconfigure.vertexai/**/**IT.java</exclude>
 								 <exclude>org.springframework.ai.autoconfigure.watsonxai/**/**IT.java</exclude>
 								 <exclude>org.springframework.ai.autoconfigure.zhipuai/**/**IT.java</exclude>
-								 
+
 								 <exclude>org.springframework.ai.autoconfigure.zhipuai/**/**IT.java</exclude>
-								 
-								 <!-- Test Containers -->								 
+
+								 <!-- Test Containers -->
 								 <exclude>org.springframework.ai.testcontainers/**/**IT.java</exclude>
 
-								 <!-- Test Docker Compose -->								 
+								 <!-- Test Docker Compose -->
 								 <exclude>package org.springframework.ai.docker.compose/**/**IT.java</exclude>
 
 								 <!-- AI Evaluation -->

spring-ai-bom/pom.xml

@@ -285,6 +285,12 @@
                 <version>${project.version}</version>
             </dependency>
 
+			<dependency>
+				<groupId>org.springframework.ai</groupId>
+				<artifactId>spring-ai-mariadb-store</artifactId>
+				<version>${project.version}</version>
+			</dependency>
+
             <!-- Utilities -->
             <dependency>
                 <groupId>org.springframework.ai</groupId>
@@ -455,6 +461,12 @@
                 <version>${project.version}</version>
             </dependency>
 
+			<dependency>
+				<groupId>org.springframework.ai</groupId>
+				<artifactId>spring-ai-mariadb-store-spring-boot-starter</artifactId>
+				<version>${project.version}</version>
+			</dependency>
+
             <dependency>
                 <groupId>org.springframework.ai</groupId>
                 <artifactId>spring-ai-stability-ai-spring-boot-starter</artifactId>

spring-ai-core/src/main/java/org/springframework/ai/observation/conventions/VectorStoreProvider.java

@@ -67,6 +67,11 @@ public enum VectorStoreProvider {
 	 */
 	HANA("hana"),
 
+	/**
+	 * Vector store provided by MariaDB.
+	 */
+	MARIADB("mariadb"),
+
 	/**
 	 * Vector store provided by Milvus.
 	 */

spring-ai-docs/src/main/antora/modules/ROOT/nav.adoc

@@ -80,6 +80,7 @@
 ** xref:api/vectordbs/chroma.adoc[]
 ** xref:api/vectordbs/elasticsearch.adoc[]
 ** xref:api/vectordbs/gemfire.adoc[GemFire]
+** xref:api/vectordbs/mariadb.adoc[]
 ** xref:api/vectordbs/milvus.adoc[]
 ** xref:api/vectordbs/mongodb.adoc[]
 ** xref:api/vectordbs/neo4j.adoc[]

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

@@ -207,6 +207,7 @@ These are the available implementations of the `VectorStore` interface:
 * xref:api/vectordbs/chroma.adoc[Chroma Vector Store] - The https://www.trychroma.com/[Chroma] vector store.
 * xref:api/vectordbs/elasticsearch.adoc[Elasticsearch Vector Store] - The https://www.elastic.co/[Elasticsearch] vector store.
 * xref:api/vectordbs/gemfire.adoc[GemFire Vector Store] - The https://tanzu.vmware.com/content/blog/vmware-gemfire-vector-database-extension[GemFire] vector store.
+* xref:api/vectordbs/mariadb.adoc[MariaDB Vector Store] - The https://mariadb.com/[MariaDB] vector store.
 * xref:api/vectordbs/milvus.adoc[Milvus Vector Store] - The https://milvus.io/[Milvus] vector store.
 * xref:api/vectordbs/mongodb.adoc[MongoDB Atlas Vector Store] - The https://www.mongodb.com/atlas/database[MongoDB Atlas] vector store.
 * xref:api/vectordbs/neo4j.adoc[Neo4j Vector Store] - The https://neo4j.com/[Neo4j] vector store.

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

@@ -0,0 +1,187 @@
+= MariaDB Vector
+
+This section walks you through setting up the MariaDB `VectorStore` to store document embeddings and perform similarity searches.
+
+link:https://mariadb.org/projects/mariadb-vector/[MariaDB vector] is part of MariaDB 11.7 and enables storing and searching over machine learning-generated embeddings.
+
+== Auto-Configuration
+
+Add the MariaDBVectorStore boot starter dependency to your project:
+
+[source,xml]
+----
+<dependency>
+	<groupId>org.springframework.ai</groupId>
+	<artifactId>spring-ai-mariadb-store-spring-boot-starter</artifactId>
+</dependency>
+----
+
+or to your Gradle `build.gradle` build file.
+
+[source,groovy]
+----
+dependencies {
+    implementation 'org.springframework.ai:spring-ai-mariadb-store-spring-boot-starter'
+}
+----
+
+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.
+
+The Vector Store also requires an `EmbeddingModel` instance to calculate embeddings for the documents.
+You can pick one of the available xref:api/embeddings.adoc#available-implementations[EmbeddingModel Implementations].
+
+For example, to use the xref:api/embeddings/openai-embeddings.adoc[OpenAI EmbeddingModel], add the following dependency to your project:
+
+[source,xml]
+----
+<dependency>
+	<groupId>org.springframework.ai</groupId>
+	<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
+</dependency>
+----
+
+or to your Gradle `build.gradle` build file.
+
+[source,groovy]
+----
+dependencies {
+    implementation 'org.springframework.ai:spring-ai-openai-spring-boot-starter'
+}
+----
+
+TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.
+Refer to the xref:getting-started.adoc#repositories[Repositories] section to add Milestone and/or Snapshot Repositories to your build file.
+
+To connect to and configure the `MariaDBVectorStore`, you need to provide access details for your instance.
+A simple configuration can be provided via Spring Boot's `application.yml`.
+
+[yml]
+----
+spring:
+  datasource:
+    url: jdbc:mariadb://localhost/db
+    username: myUser
+    password: myPassword
+  ai:
+	vectorstore:
+	  mariadbvector:
+		distance-type: COSINE
+		dimensions: 1536
+----
+
+TIP: If you run MariaDBvector as a Spring Boot dev service via link:https://docs.spring.io/spring-boot/reference/features/dev-services.html#features.dev-services.docker-compose[Docker Compose]
+or link:https://docs.spring.io/spring-boot/reference/features/dev-services.html#features.dev-services.testcontainers[Testcontainers],
+you don't need to configure URL, username and password since they are autoconfigured by Spring Boot.
+
+TIP: Check the list of xref:#mariadbvector-properties[configuration parameters] to learn about the default values and configuration options.
+
+Now you can auto-wire the `MariaDBVectorStore` in your application and use it
+
+[source,java]
+----
+@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 PGVector
+vectorStore.add(documents);
+
+// Retrieve documents similar to a query
+List<Document> results = this.vectorStore.similaritySearch(SearchRequest.query("Spring").withTopK(5));
+----
+
+[[mariadbvector-properties]]
+=== Configuration properties
+
+You can use the following properties in your Spring Boot configuration to customize the MariaDB vector store.
+
+[cols="2,5,1",stripes=even]
+|===
+|Property| Description | Default value
+
+|`spring.ai.vectorstore.mariadb.distance-type`| Search distance type. Defaults to `COSINE`. But 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 the PgVectorStore will retrieve the dimensions form the provided `EmbeddingModel`. Dimensions are set to the embedding column the on table creation. If you change the dimensions your would have to re-create the vector_store table as well. | -
+|`spring.ai.vectorstore.mariadb.remove-existing-vector-store-table` | Deletes the existing `vector_store` table on start up.  | false
+|`spring.ai.vectorstore.mariadb.initialize-schema` | Whether to initialize the required schema | 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
+
+|===
+
+TIP: 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.
+
+== Metadata filtering
+
+You can leverage the generic, portable link:https://docs.spring.io/spring-ai/reference/api/vectordbs.html#_metadata_filters[metadata filters] with the MariaDB Vector store.
+
+For example, you can use either the text expression language:
+
+[source,java]
+----
+vectorStore.similaritySearch(
+    SearchRequest.defaults()
+    .withQuery("The World")
+    .withTopK(TOP_K)
+    .withSimilarityThreshold(SIMILARITY_THRESHOLD)
+    .withFilterExpression("author in ['john', 'jill'] && article_type == 'blog'"));
+----
+
+or programmatically using the `Filter.Expression` DSL:
+
+[source,java]
+----
+FilterExpressionBuilder b = new FilterExpressionBuilder();
+
+vectorStore.similaritySearch(SearchRequest.defaults()
+    .withQuery("The World")
+    .withTopK(TOP_K)
+    .withSimilarityThreshold(SIMILARITY_THRESHOLD)
+    .withFilterExpression(b.and(
+        b.in("author","john", "jill"),
+        b.eq("article_type", "blog")).build()));
+----
+
+NOTE: These filter expressions are converted into the equivalent PgVector filters.
+
+== Manual Configuration
+
+Instead of using the Spring Boot auto-configuration, you can manually configure the `MariaDBVectorStore`.
+For this you need to add the MariaDB connector and `JdbcTemplate` auto-configuration dependencies to your project:
+
+[source,xml]
+----
+<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>
+----
+
+TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.
+
+To configure MariaDB Vector in your application, you can use the following setup:
+
+[source,java]
+----
+@Bean
+public VectorStore vectorStore(JdbcTemplate jdbcTemplate, EmbeddingModel embeddingModel) {
+	return new MariaDBVectorStore(jdbcTemplate, embeddingModel);
+}
+----

spring-ai-docs/src/main/antora/modules/ROOT/pages/index.adoc

@@ -1,5 +1,4 @@
 [[introduction]]
-= Introduction
 
 image::spring_ai_logo_with_text.svg[Integration Problem, width=300, align="left"]
 
@@ -26,7 +25,7 @@ Spring AI provides the following features:
 ** xref:api/audio/speech.adoc[Text to Speech]
 ** xref:api/moderation[Moderation]
 * xref:api/structured-output-converter.adoc[Structured Outputs] - Mapping of AI Model output to POJOs.
-* Support for all major xref:api/vectordbs.adoc[Vector Database providers] such as Apache Cassandra, Azure Cosmos DB, Azure Vector Search, Chroma, Elasticsearch, GemFire, Milvus, MongoDB Atlas, Neo4j, OpenSearch, Oracle, PostgreSQL/PGVector, PineCone, Qdrant, Redis, SAP Hana, Typesense and Weaviate.
+* Support for all major xref:api/vectordbs.adoc[Vector Database providers] such as Apache Cassandra, Azure Cosmos DB, Azure Vector Search, Chroma, Elasticsearch, GemFire, MariaDB, Milvus, MongoDB Atlas, Neo4j, OpenSearch, Oracle, PostgreSQL/PGVector, PineCone, Qdrant, Redis, SAP Hana, Typesense and Weaviate.
 * Portable API across Vector Store providers, including a novel SQL-like metadata filter API.
 * xref:api/functions.adoc[Tools/Function Calling] - permits the model to request the execution of client-side tools and functions, thereby accessing necessary real-time information as required.
 * xref:observability/index.adoc[Observability] - Provides insights into AI-related operations.

spring-ai-spring-boot-autoconfigure/pom.xml

@@ -178,6 +178,20 @@
 			<optional>true</optional>
 		</dependency>
 
+		<!-- MariaDB Vector Store-->
+		<dependency>
+			<groupId>org.springframework.ai</groupId>
+			<artifactId>spring-ai-mariadb-store</artifactId>
+			<version>${project.parent.version}</version>
+			<optional>true</optional>
+		</dependency>
+		<dependency>
+			<groupId>org.mariadb.jdbc</groupId>
+			<artifactId>mariadb-java-client</artifactId>
+			<version>${mariadb.version}</version>
+			<optional>true</optional>
+		</dependency>
+
 		<!-- Chroma Vector Store -->
 		<dependency>
 			<groupId>org.springframework.ai</groupId>
@@ -457,6 +471,12 @@
 			<scope>test</scope>
 		</dependency>
 
+		<dependency>
+			<groupId>org.testcontainers</groupId>
+			<artifactId>mariadb</artifactId>
+			<scope>test</scope>
+		</dependency>
+
 		<dependency>
 			<groupId>org.testcontainers</groupId>
 			<artifactId>junit-jupiter</artifactId>