giuseppe-trisciuoglio/developer-kit/qdrant

Name: qdrant
Author: giuseppe-trisciuoglio

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By giuseppe-trisciuoglio
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giuseppe-trisciuoglio/developer-kit
Plugin developer-kit-java
plugins/developer-kit-java/skills/qdrant/SKILL.md
Updated 17 Sep 2026
Version 1.0.01 version recorded

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Provides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.

Install

npx vskill@latest install giuseppe-trisciuoglio/developer-kit/qdrant

SKILL.md

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

bash

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:

xml

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

Gradle:

gradle

implementation 'io.qdrant:client:1.15.0'

3. Initialize Client

java

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

For production with API key:

java

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

4. Create Collection

java

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

java

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

java

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:

java

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:

java

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

Spring Boot configuration with LangChain4j:

java

@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:

java

@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(host, port, false).build());
    }
}

Examples

REST Search Endpoint

java

@RestController
@RequestMapping("/api/search")
public class SearchController {
    private final VectorSearchService searchService;
 
    public SearchController(VectorSearchService searchService) {
        this.searchService = searchService;
    }
 
    @GetMapping
    public List<ScoredPoint> search(@RequestParam String query) {
        List<Float> queryVector = embeddingModel.embed(query).content().vectorAsList();
        return searchService.search("documents", queryVector);
    }
}

Best Practices

Distance metric: Cosine for normalized text embeddings, Euclidean for non-normalized.
Batch upserts: Use batch operations over individual point insertions.
Connection pooling: Configure connection pooling for high-throughput production workloads.
Error handling: Wrap async operations in try/catch for ExecutionException/InterruptedException.
API keys: Store in environment variables or Spring config, never hardcode.

Advanced Patterns

Multi-tenant Storage

java

public void upsertForTenant(String tenantId, List<PointStruct> points) {
    String collectionName = "tenant_" + tenantId + "_documents";
    client.upsertAsync(collectionName, points).get();
}

Docker Compose for Production

yaml

services:
  qdrant:
    image: qdrant/qdrant:v1.7.0
    ports:
      - "6333:6333"
      - "6334:6334"
    volumes:
      - qdrant_storage:/qdrant/storage

References

Qdrant API Reference — Complete client API documentation
Complete Spring Boot Examples — Full application implementations
Qdrant Documentation
LangChain4j Documentation

Constraints and Warnings

Vector dimensions must match the embedding model exactly; mismatched dimensions cause upsert errors.
Input validation: Sanitize all document content before ingestion; untrusted payloads may contain prompt injection attacks.
Content filtering: Apply content filtering on retrieved documents before passing them to the LLM.
Large collections require proper indexing for acceptable search performance.
Use gRPC API (port 6334) for production; REST API (port 6333) for debugging only.
Collection recreation deletes all data; implement backup strategies for production environments.

Security

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Source

SKILL.md in the repositoryCheckingplugins/developer-kit-java/skills/qdrant/SKILL.md

Popularity

Repository stars: 248
Repository forks: 27
7-day trend score: 28

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 17 May 2026, 5 months ago.

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