支持使用 MariaDB 作为向量库

spring-ai-vector/pom.xml

@@ -13,6 +13,7 @@
     <modules>
         <module>spring-ai-vector-milvus</module>
         <module>spring-ai-vector-redis</module>
+        <module>spring-ai-vector-mariadb</module>
     </modules>
 
     <dependencies>

spring-ai-vector/spring-ai-vector-mariadb/README.md

@@ -0,0 +1,73 @@
+## spring-ai-vector-mariadb 
+
+该工程模块主要是集成 mariadb 的向量存储功能，提供了一个使用 mariadb 存储向量并执行相似性搜索的简单示例。
+
+[mariadb使用说明](https://mariadb.org/documentation/) 
+
+- 关系数据库简介
+-   10分钟MariaDB服务器入门
+-   SQL 语句列表
+-   有用的 MariaDB 服务器查询
+-  MariaDB 服务器文档
+
+## 前提条件
+
+### 1、mariadb 数据库部署与初始化
+
+mariadb 是一个开源的向量数据库，用于存储和检索高维向量数据。本项目是使用 Docker 来运行 mariadb，当然你也可以选择其他方式安装 mariadb或者使用已经部署好的 mariadb 服务。
+[mariadb镜像地址](https://hub.docker.com/_/mariadb)
+```
+
+② 一个 `EmbeddingModel` 实例，用于计算文档嵌入。有多个[选项](https://docs.spring.io/spring-ai/reference/api/embeddings.html#available-implementations)可供选择
+
+③ 一个 API 密钥，给 EmbeddingModel 用于生成向量数据
+
+
+## 自动配置
+Spring AI 为 Mariadb 向量数据库提供了 Spring Boot 自动配置。要启用它~~，请将以下依赖添加到项目的 Maven pom.xml 文件中：~~
+```
+```xml
+<dependencies>
+    <dependency>
+        <groupId>org.springframework.ai</groupId>
+        <artifactId>spring-ai-starter-vector-store-mariadb</artifactId>
+    </dependency>
+</dependencies>
+````
+
+## 配置文件
+
+在你启动项目之前，你需要修改 `application.yml` 文件。
+
+```yaml
+server:
+  port: 8080
+
+spring:
+  datasource:
+    url: ${BASE_HOST}
+    username:  ${BASE_NAME}
+    password:  ${BASE_PWD}
+    driver-class-name: org.mariadb.jdbc.Driver
+  ai:
+    vectorstore:
+      mariadb:
+        # 启用模式初始化
+        initialize-schema: true
+        # 设置距离计算类型为余弦相似度
+        distance-type: COSINE
+        # 定义向量维度为1536
+        dimensions: 1536
+    openai:
+      api-key: ${API_KEY}
+      embedding:
+        base-url: https://dashscope.aliyuncs.com/compatible-mode/v1
+        embeddings-path: /embeddings
+        options:
+          model: text-embedding-v4
+
+```
+修改完成之后即可以在 IDEA 中启动单元测试。
+
+
+

spring-ai-vector/spring-ai-vector-mariadb/pom.xml

@@ -0,0 +1,38 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>com.glmapper</groupId>
+    <artifactId>spring-ai-vector</artifactId>
+    <version>0.0.1</version>
+  </parent>
+
+  <artifactId>spring-ai-vector-mariadb</artifactId>
+  <packaging>jar</packaging>
+
+  <properties>
+    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+  </properties>
+
+  <dependencies>
+
+    <dependency>
+      <groupId>org.springframework.ai</groupId>
+      <artifactId>spring-ai-starter-vector-store-mariadb</artifactId>
+    <exclusions>
+      <exclusion>
+        <groupId>org.mariadb.jdbc</groupId>
+        <artifactId>mariadb-java-client</artifactId>
+      </exclusion>
+    </exclusions>
+    </dependency>
+    <!-- 3.3版本缺少enquoteIdentifier方法,  springAI 自动调用失败,引入最新版本client -->
+    <dependency>
+      <groupId>org.mariadb.jdbc</groupId>
+      <artifactId>mariadb-java-client</artifactId>
+      <version>3.5.2</version>
+    </dependency>
+  </dependencies>
+</project>

spring-ai-vector/spring-ai-vector-mariadb/src/main/java/com/glmapper/ai/vector/MariadbVectorApplication.java

@@ -0,0 +1,15 @@
+package com.glmapper.ai.vector;
+
+import org.springframework.boot.SpringApplication;
+import org.springframework.boot.autoconfigure.SpringBootApplication;
+
+/**
+ * @author siyuan
+ * @since 2025/6/14
+ */
+@SpringBootApplication
+public class MariadbVectorApplication {
+    public static void main(String[] args) {
+        SpringApplication.run(MariadbVectorApplication.class, args);
+    }
+}

spring-ai-vector/spring-ai-vector-mariadb/src/main/java/com/glmapper/ai/vector/storage/VectorStoreStorage.java

@@ -0,0 +1,38 @@
+package com.glmapper.ai.vector.storage;
+
+import lombok.RequiredArgsConstructor;
+import org.springframework.ai.document.Document;
+import org.springframework.ai.vectorstore.SearchRequest;
+import org.springframework.ai.vectorstore.VectorStore;
+import org.springframework.stereotype.Component;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Set;
+
+/**
+ * @author siyuan
+ * @since 2025/6/14
+ */
+@Component
+@RequiredArgsConstructor
+public class VectorStoreStorage {
+
+    private final VectorStore vectorStore;
+
+
+    public void delete(Set<String> ids) {
+        vectorStore.delete(new ArrayList<>(ids));
+    }
+
+    public void store(List<Document> documents) {
+        if (documents == null || documents.isEmpty()) {
+            return;
+        }
+        vectorStore.add(documents);
+    }
+
+    public List<Document> search(String query) {
+        return vectorStore.similaritySearch(SearchRequest.builder().query(query).topK(5).build());
+    }
+}

spring-ai-vector/spring-ai-vector-mariadb/src/main/resources/application.yml

@@ -0,0 +1,27 @@
+server:
+  port: 8080
+
+spring:
+  application:
+    name: redis-vector-store
+  datasource:
+    url: ${BASE_HOST}
+    username:  ${BASE_NAME}
+    password:  ${BASE_PWD}
+    driver-class-name: org.mariadb.jdbc.Driver
+  ai:
+    vectorstore:
+      mariadb:
+        # 启用模式初始化
+        initialize-schema: true
+        # 设置距离计算类型为余弦相似度
+        distance-type: COSINE
+        # 定义向量维度为1536
+        dimensions: 1536
+    openai:
+      api-key: ${API_KEY}
+      embedding:
+        base-url: https://dashscope.aliyuncs.com/compatible-mode/v1
+        embeddings-path: /embeddings
+        options:
+          model: text-embedding-v4

spring-ai-vector/spring-ai-vector-mariadb/src/test/java/storage/VectorStoreStorageTest.java

@@ -0,0 +1,58 @@
+package storage;
+
+import com.glmapper.ai.vector.MariadbVectorApplication;
+import com.glmapper.ai.vector.storage.VectorStoreStorage;
+import org.junit.jupiter.api.AfterEach;
+import org.junit.jupiter.api.Assertions;
+import org.junit.jupiter.api.Test;
+import org.springframework.ai.document.Document;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.boot.test.context.SpringBootTest;
+import org.springframework.test.context.ActiveProfiles;
+
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import java.util.stream.Collectors;
+
+/**
+ * 测试 基于 mariadb 的 VectorStoreStorage 的存储和搜索功能
+ *
+ * @author siyuan
+ * @since 2025/6/14
+ */
+@SpringBootTest(
+        classes = MariadbVectorApplication.class,
+        webEnvironment = SpringBootTest.WebEnvironment.RANDOM_PORT
+)
+@ActiveProfiles("test")
+public class VectorStoreStorageTest {
+
+    @Autowired
+    private VectorStoreStorage vectorStoreStorage;
+
+    //prepare test data
+    private static final 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")));
+
+    @AfterEach
+    public void cleanUp() {
+        // clear the vector store after each test
+        Set<String> ids = documents.stream().map(Document::getId)
+                .collect(Collectors.toSet());
+        vectorStoreStorage.delete(ids);
+    }
+
+    @Test
+    public void testStoreAndSearch() {
+        // store documents
+        vectorStoreStorage.store(documents);
+        // do search
+        String query = "Spring AI rocks!!";
+        List<Document> results = vectorStoreStorage.search(query);
+        // assertions
+        Assertions.assertFalse(results.isEmpty(), "搜索结果不应该为空");
+    }
+}

spring-ai-vector/spring-ai-vector-mariadb/src/test/resources/application-test.yml

@@ -0,0 +1,28 @@
+server:
+  port: 8080
+
+spring:
+  application:
+    name: redis-vector-store
+  datasource:
+    url: jdbc:mariadb://localhost:3308/vector_test
+    username: root
+    password: root
+    driver-class-name: org.mariadb.jdbc.Driver
+  ai:
+    vectorstore:
+      mariadb:
+        # 启用模式初始化
+        initialize-schema: false
+        # 设置距离计算类型为余弦相似度
+        distance-type: COSINE
+        # 定义向量维度为1536
+        dimensions: 1536
+    openai:
+      api-key: ${API_KEY}
+      embedding:
+        # doc reference: https://bailian.console.aliyun.com/?switchAgent=12095181&productCode=p_efm&switchUserType=3&tab=api#/api/?type=model&url=https%3A%2F%2Fhelp.aliyun.com%2Fdocument_detail%2F2712515.html&renderType=iframe
+        base-url: https://dashscope.aliyuncs.com/compatible-mode/v1
+        embeddings-path: /embeddings
+        options:
+          model: text-embedding-v4