qdrant

Qdrant Vector Database Integration

Overview

Qdrant is an AI-native vector database for semantic search and similarity retrieval. This skill provides patterns for integrating Qdrant with Java applications, focusing on Spring Boot and LangChain4j integration.

When to Use

• Semantic search or recommendation systems in Spring Boot applications
• RAG pipelines with Java and LangChain4j
• Vector database integration for AI/ML applications
• High-performance similarity search with filtered queries

Instructions

1. Deploy Qdrant with Docker

docker run -p 6333:6333 -p 6334:6334 \
    -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \
    qdrant/qdrant

Access: REST API at http://localhost:6333, gRPC at http://localhost:6334.

2. Add Dependencies

Maven:

<dependency>
    <groupId>io.qdrant</groupId>
    <artifactId>client</artifactId>
    <version>1.15.0</version>
</dependency>

Gradle:

implementation 'io.qdrant:client:1.15.0'

3. Initialize Client

QdrantClient client = new QdrantClient(
    QdrantGrpcClient.newBuilder("localhost").build());

For production with API key:

QdrantClient client = new QdrantClient(
    QdrantGrpcClient.newBuilder("localhost", 6334, false)
        .withApiKey("YOUR_API_KEY")
        .build());

4. Create Collection

client.createCollectionAsync("search-collection",
    VectorParams.newBuilder()
        .setDistance(Distance.Cosine)
        .setSize(384)
        .build()
).get();

Validation: Verify the collection was created by checking client.getCollectionAsync("search-collection").get().

5. Upsert Vectors

List<PointStruct> points = List.of(
    PointStruct.newBuilder()
        .setId(id(1))
        .setVectors(vectors(0.05f, 0.61f, 0.76f, 0.74f))
        .putAllPayload(Map.of("title", value("Spring Boot Documentation")))
        .build()
);
client.upsertAsync("search-collection", points).get();

Validation: Check that client.upsertAsync(...).get() completes without throwing.