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.

6. Search Vectors

List<ScoredPoint> results = client.queryAsync(
    QueryPoints.newBuilder()
        .setCollectionName("search-collection")
        .setLimit(5)
        .setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
        .build()
).get();

Filtered search:

List<ScoredPoint> results = client.searchAsync(
    SearchPoints.newBuilder()
        .setCollectionName("search-collection")
        .addAllVector(List.of(0.62f, 0.12f, 0.53f, 0.12f))
        .setFilter(Filter.newBuilder()
            .addMust(range("category", Range.newBuilder().setEq("docs").build()))
            .build())
        .setLimit(5)
        .build()).get();

LangChain4j Integration

For RAG pipelines, use LangChain4j's high-level abstractions:

EmbeddingStore<TextSegment> embeddingStore = QdrantEmbeddingStore.builder()
    .collectionName("rag-collection")
    .host("localhost")
    .port(6334)
    .apiKey("YOUR_API_KEY")
    .build();

Spring Boot configuration with LangChain4j:

@Bean
public EmbeddingStore<TextSegment> embeddingStore() {
    return QdrantEmbeddingStore.builder()
        .collectionName("rag-collection")
        .host(host)
        .port(port)
        .build();
}

@Bean
public EmbeddingModel embeddingModel() {
    return new AllMiniLmL6V2EmbeddingModel();
}

Spring Boot Integration

Inject the client via configuration:

@Configuration
public class QdrantConfig {
    @Value("${qdrant.host:localhost}")
    private String host;

    @Value("${qdrant.port:6334}")
    private int port;

    @Bean
    public QdrantClient qdrantClient() {
        return new QdrantClient(
            QdrantGrpcClient.newBuilder
how to use qdrant
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How to use qdrant on Cursor
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1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add qdrant
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill qdrantcopy
The skills CLI fetches qdrant from GitHub repository giuseppe-trisciuoglio/developer-kit and configures it for Cursor.
3
Select Cursor when prompted
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◆ Which agents do you want to install to?
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│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/qdrant
Reload or restart Cursor to activate qdrant. Access the skill through slash commands (e.g., /qdrant) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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