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Revert "docs: Added Spring AI integration (#634)" (#649)

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---
title: Spring AI
weight: 2200
---
# Spring AI
[Spring AI](https://docs.spring.io/spring-ai/reference/) is a Java framework that provides a [Spring-friendly](https://spring.io/) 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`.
```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.
```xml
<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.
```java
@Bean
public QdrantVectorStoreConfig qdrantVectorStoreConfig() {
return QdrantVectorStoreConfig.builder()
.withHost("<QDRANT_HOSTNAME>")
.withPort(<QDRANT_GRPC_PORT>)
.withCollectionName("<QDRANT_COLLECTION_NAME>")
.withApiKey("<QDRANT_API_KEY>")
.build();
}
```
<aside role="status">You'll need to <a href="/documentation/concepts/collections/#create-a-collection">create a collection</a> with the appropriate vector dimensions and configurations in advance.</aside>
Build the vector store using the config and any of the support [Spring AI embedding providers](https://docs.spring.io/spring-ai/reference/api/embeddings.html#available-implementations).
```java
@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](https://docs.spring.io/spring-ai/reference/index.html)