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* docs: TestContainers integration * docs: refactor Spring AI docs * docs: review changes * Apply suggestions from code review Co-authored-by: Eddú Meléndez Gonzales <eddu.melendez@gmail.com> * Apply suggestions from code review Co-authored-by: Mike Jang <michael.jang@qdrant.io> * Update qdrant-landing/content/documentation/frameworks/spring-ai.md Co-authored-by: Mike Jang <michael.jang@qdrant.io> * chore: Updated preview image * Update qdrant-landing/content/documentation/frameworks/testcontainers.md * docs: Commented out Node/Python modules --------- Co-authored-by: Eddú Meléndez Gonzales <eddu.melendez@gmail.com> Co-authored-by: Mike Jang <michael.jang@qdrant.io>
70 lines
2.5 KiB
Markdown
70 lines
2.5 KiB
Markdown
---
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title: Spring AI
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weight: 2200
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---
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# Spring AI
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[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.
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Qdrant is available as supported vector database for use within your Spring AI projects.
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## Installation
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You can find the Spring AI installation instructions [here](https://docs.spring.io/spring-ai/reference/getting-started.html).
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Add the Qdrant boot starter package.
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```xml
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-qdrant-store-spring-boot-starter</artifactId>
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</dependency>
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```
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## Usage
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Configure Qdrant with Spring Boot’s `application.properties`.
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```
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spring.ai.vectorstore.qdrant.host=<host of your qdrant instance>
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spring.ai.vectorstore.qdrant.port=<the GRPC port of your qdrant instance>
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spring.ai.vectorstore.qdrant.api-key=<your api key>
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spring.ai.vectorstore.qdrant.collection-name=<The name of the collection to use in Qdrant>
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```
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Learn more about these options in the [configuration reference](https://docs.spring.io/spring-ai/reference/api/vectordbs/qdrant.html#qdrant-vectorstore-properties).
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Or you can set up the Qdrant vector store with the `QdrantVectorStoreConfig` options.
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```java
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@Bean
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public QdrantVectorStoreConfig qdrantVectorStoreConfig() {
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return QdrantVectorStoreConfig.builder()
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.withHost("<QDRANT_HOSTNAME>")
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.withPort(<QDRANT_GRPC_PORT>)
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.withCollectionName("<QDRANT_COLLECTION_NAME>")
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.withApiKey("<QDRANT_API_KEY>")
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.build();
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}
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```
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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).
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```java
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@Bean
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public VectorStore vectorStore(QdrantVectorStoreConfig config, EmbeddingClient embeddingClient) {
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return new QdrantVectorStore(config, embeddingClient);
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}
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```
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You can now use the `VectorStore` instance backed by Qdrant as a vector store in the Spring AI APIs.
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<aside role="status">If the collection is not <a href="/documentation/concepts/collections/#create-a-collection">created in advance</a>, <code>QdrantVectorStore</code> will attempt to create one using cosine similarity and the dimension of the configured <code>EmbeddingClient</code>.</aside>
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## 📚 Further Reading
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- Spring AI [Qdrant reference](https://docs.spring.io/spring-ai/reference/api/vectordbs/qdrant.html)
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- Spring AI [API reference](https://docs.spring.io/spring-ai/reference/index.html)
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