landing_page/qdrant-landing/content/documentation/frameworks/spring-ai.md

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title	weight
Spring AI	2200

Spring AI

Spring AI is a Java framework that provides a Spring-friendly API and abstractions for developing AI applications.

Qdrant is available as supported vector database for use within your Spring AI projects.

Installation

To acquire Spring AI artifacts, declare the Spring Snapshot repository in your pom.xml.

<repository>
   <id>spring-snapshots</id>
   <name>Spring Snapshots</name>
   <url>https://repo.spring.io/snapshot</url>
   <releases>
      <enabled>false</enabled>
   </releases>
</repository>

Add the spring-ai-qdrant package.

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-qdrant</artifactId>
    <version>VERSION</version>
</dependency>

Usage

You can set up the Qdrant vector store with the QdrantVectorStoreConfig options.

@Bean
public QdrantVectorStoreConfig qdrantVectorStoreConfig() {

    return QdrantVectorStoreConfig.builder()
        .withHost("<QDRANT_HOSTNAME>")
        .withPort(<QDRANT_GRPC_PORT>)
        .withCollectionName("<QDRANT_COLLECTION_NAME>")
        .withApiKey("<QDRANT_API_KEY>")
        .build();
}

You'll need to create a collection with the appropriate vector dimensions and configurations in advance.

Build the vector store using the config and any of the support Spring AI embedding providers.

@Bean
public VectorStore vectorStore(QdrantVectorStoreConfig config, EmbeddingClient embeddingClient) {
    return new QdrantVectorStore(config, embeddingClient);
}

You can now use the VectorStore instance backed by Qdrant as a vector store in the Spring AI APIs.

Further Reading

• 📚 Spring AI reference