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

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

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

@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 Milvus Vector Store
vectorStore.add(documents);

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

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

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

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

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());

MilvusSearchRequest request = MilvusSearchRequest.milvusBuilder()
    .query("sample query")
    .topK(5)
    .similarityThreshold(0.7)
    .nativeExpression("metadata[\"age\"] > 30") // Overrides filterExpression if both are set
    .filterExpression("age <= 30") // Ignored if nativeExpression is set
    .searchParamsJson("{\"nprobe\":128}")
    .build();
List results = vectorStore.similaritySearch(request);

MilvusSearchRequest request = MilvusSearchRequest.milvusBuilder()
    .query("sample query")
    .topK(5)
    .nativeExpression("metadata['category'] == 'science'")
    .build();

MilvusSearchRequest request = MilvusSearchRequest.milvusBuilder()
    .query("sample query")
    .topK(5)
    .searchParamsJson("{\"nprobe\":128}")
    .build();

docker-compose up

docker-compose down; rm -Rf ./volumes

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

MilvusVectorStore vectorStore = context.getBean(MilvusVectorStore.class);
Optional<MilvusServiceClient> nativeClient = vectorStore.getNativeClient();

if (nativeClient.isPresent()) {
    MilvusServiceClient client = nativeClient.get();
    // Use the native client for Milvus-specific operations
}