Milvus

Milvus 是一个开源向量数据库，在数据科学和机器学习领域引起了极大关注。其突出特点之一是对向量索引和查询的强大支持。Milvus 采用最先进、前沿的算法来加速搜索进程，使其即使在处理海量数据集时也能高效地检索相似向量。

Milvus is an open-source vector database that has garnered significant attention in the fields of data science and machine learning. One of its standout features lies in its robust support for vector indexing and querying. Milvus employs state-of-the-art, cutting-edge algorithms to accelerate the search process, making it exceptionally efficient at retrieving similar vectors, even when handling extensive datasets.

Prerequisites

  • A running Milvus instance. The following options are available:

  • If required, an API key for the EmbeddingClient to generate the embeddings stored by the MilvusVectorStore.

Dependencies

然后将 Milvus VectorStore 启动器依赖项添加到你的项目：

Then add the Milvus VectorStore boot starter dependency to your project:

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

或添加到 Gradle build.gradle 构建文件中。

or to your Gradle build.gradle build file.

dependencies {
    implementation 'org.springframework.ai:spring-ai-milvus-store-spring-boot-starter'
}

向量存储还要求 EmbeddingClient 实例为文档计算嵌入。您可以选择一个可用的 EmbeddingClient Implementations。

The Vector Store, also requires an EmbeddingClient instance to calculate embeddings for the documents. You can pick one of the available EmbeddingClient Implementations.

请参阅 Dependency Management 部分，将 Spring AI BOM 添加到您的构建文件中。请参阅 Repositories 部分，将 Milestone 和/或快照存储库添加到您的构建文件中。

Refer to the Dependency Management section to add the Spring AI BOM to your build file. Refer to the Repositories section to add Milestone and/or Snapshot Repositories to your build file.

要连接并配置 MilvusVectorStore，你需要提供你的实例的访问详细信息。可以使用 Spring Boot 的 `application.yml`提供简单配置

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

spring:
	ai:
		vectorstore:
			milvus:
				client:
					host: "localhost"
					port: 19530
					username: "root"
					password: "milvus"
				databaseName: "default"
				collectionName: "vector_store"
				embeddingDimension: 1536
				indexType: IVF_FLAT
				metricType: COSINE

查看 configuration parameters 列表了解默认值和配置选项。

Check the list of milvus-properties to learn about the default values and configuration options.

现在，你可以在应用程序中自动装配 Milvus Vector Store 并使用它了

Now you can Auto-wire the Milvus Vector Store in your application and use it

@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 = vectorStore.similaritySearch(SearchRequest.query("Spring").withTopK(5));

Manual Configuration

除了使用 Spring Boot 自动配置外，还可以手动配置 MilvusVectorStore。将以下依赖项添加到你的项目：

Instead of using the Spring Boot auto-configuration, you can manually configure the MilvusVectorStore. To add the following dependencies to your project:

<dependency>
	<groupId>org.springframework.ai</groupId>
	<artifactId>spring-ai-milvus-store</artifactId>
</dependency>

  1. 参见 Dependency Management 部分，将 Spring AI BOM 添加到你的构建文件中。

Refer to the Dependency Management section to add the Spring AI BOM to your build file.

可以在应用程序中使用以下设置配置 MilvusVectorStore：

To configure MilvusVectorStore in your application, you can use the following setup:

	@Bean
	public VectorStore vectorStore(MilvusServiceClient milvusClient, EmbeddingClient embeddingClient) {
		MilvusVectorStoreConfig config = MilvusVectorStoreConfig.builder()
			.withCollectionName("test_vector_store")
			.withDatabaseName("default")
			.withIndexType(IndexType.IVF_FLAT)
			.withMetricType(MetricType.COSINE)
			.build();
		return new MilvusVectorStore(milvusClient, embeddingClient, config);
	}

	@Bean
	public MilvusServiceClient milvusClient() {
		return new MilvusServiceClient(ConnectParam.newBuilder()
			.withAuthorization("minioadmin", "minioadmin")
			.withUri(milvusContainer.getEndpoint())
			.build());
	}

Metadata filtering

您可以将通用的、可移植的 metadata filters 与 Milvus 存储结合使用。

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

例如，你可以使用文本表达式语言：

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

vectorStore.similaritySearch(
    SearchRequest.defaults()
    .withQuery("The World")
    .withTopK(TOP_K)
    .withSimilarityThreshold(SIMILARITY_THRESHOLD)
    .withFilterExpression("author in ['john', 'jill'] && article_type == 'blog'"));

或使用 Filter.Expression DSL 以编程方式：

or programmatically using the Filter.Expression DSL:

FilterExpressionBuilder b = new FilterExpressionBuilder();

vectorStore.similaritySearch(SearchRequest.defaults()
    .withQuery("The World")
    .withTopK(TOP_K)
    .withSimilarityThreshold(SIMILARITY_THRESHOLD)
    .withFilterExpression(b.and(
        b.in("john", "jill"),
        b.eq("article_type", "blog")).build()));

这些筛选表达式被转换为等同的 PgVector 筛选。

These filter expressions are converted into the equivalent PgVector filters.

Milvus VectorStore properties

您可以在 Spring Boot 配置中使用以下属性来定制 Milvus 向量存储。

You can use the following properties in your Spring Boot configuration to customize the Milvus vector store.

Property Description Default value

spring.ai.vectorstore.milvus.database-name

The name of the Milvus database to use.

default

spring.ai.vectorstore.milvus.collection-name

Milvus collection name to store the vectors

vector_store

spring.ai.vectorstore.milvus.embedding-dimension

The dimension of the vectors to be stored in the Milvus collection.

1536

spring.ai.vectorstore.milvus.index-type

The type of the index to be created for the Milvus collection.

IVF_FLAT

spring.ai.vectorstore.milvus.metric-type

The metric type to be used for the Milvus collection.

COSINE

spring.ai.vectorstore.milvus.index-parameters

The index parameters to be used for the Milvus collection.

{"nlist":1024}

spring.ai.vectorstore.milvus.client.host

The name or address of the host.

localhost

spring.ai.vectorstore.milvus.client.port

The connection port.

19530

spring.ai.vectorstore.milvus.client.uri

The uri of Milvus instance

-

spring.ai.vectorstore.milvus.client.token

Token serving as the key for identification and authentication purposes.

-

spring.ai.vectorstore.milvus.client.connect-timeout-ms

Connection timeout value of client channel. The timeout value must be greater than zero .

10000

spring.ai.vectorstore.milvus.client.keep-alive-time-ms

Keep-alive time value of client channel. The keep-alive value must be greater than zero.

55000

spring.ai.vectorstore.milvus.client.keep-alive-timeout-ms

The keep-alive timeout value of client channel. The timeout value must be greater than zero.

20000

spring.ai.vectorstore.milvus.client.rpc-deadline-ms

Deadline for how long you are willing to wait for a reply from the server. With a deadline setting, the client will wait when encounter fast RPC fail caused by network fluctuations. The deadline value must be larger than or equal to zero.

0

spring.ai.vectorstore.milvus.client.client-key-path

The client.key path for tls two-way authentication, only takes effect when "secure" is true

-

spring.ai.vectorstore.milvus.client.client-pem-path

The client.pem path for tls two-way authentication, only takes effect when "secure" is true

-

spring.ai.vectorstore.milvus.client.ca-pem-path

The ca.pem path for tls two-way authentication, only takes effect when "secure" is true

-

spring.ai.vectorstore.milvus.client.server-pem-path

server.pem path for tls one-way authentication, only takes effect when "secure" is true.

-

spring.ai.vectorstore.milvus.client.server-name

Sets the target name override for SSL host name checking, only takes effect when "secure" is True. Note: this value is passed to grpc.ssl_target_name_override

-

spring.ai.vectorstore.milvus.client.secure

Secure the authorization for this connection, set to True to enable TLS.

false

spring.ai.vectorstore.milvus.client.idle-timeout-ms

Idle timeout value of client channel. The timeout value must be larger than zero.

24h

spring.ai.vectorstore.milvus.client.username

The username and password for this connection.

root

spring.ai.vectorstore.milvus.client.password

The password for this connection.

milvus

Starting Milvus Store

在 src/test/resources/ 文件夹中运行：

From within the src/test/resources/ folder run:

docker-compose up

要清除环境：

To clean the environment:

docker-compose down; rm -Rf ./volumes

然后连接到 http://localhost:19530 上的向量存储，或者用于管理 http://localhost:9001（用户：minioadmin，密码：minioadmin）

Then connect to the vector store on http://localhost:19530 or for management http://localhost:9001 (user: minioadmin, pass: minioadmin)

Troubleshooting

如果 Docker 抱怨资源，则执行：

If Docker complains about resources, then execute:

docker system prune --all --force --volumes