Agent skill

qdrant

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.

View SKILL.md on GitHub Repository
Stars 192
Forks 20

Install this agent skill to your Project

npx add-skill https://github.com/giuseppe-trisciuoglio/developer-kit/tree/main/plugins/developer-kit-java/skills/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

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.

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

giuseppe-trisciuoglio/developer-kit

aws-cli-beast

Provides advanced AWS CLI patterns for managing EC2, Lambda, S3, DynamoDB, RDS, VPC, IAM, and CloudWatch. Generates bulk operation scripts, automates cross-service workflows, validates security configurations, and executes JMESPath queries for complex filtering. Triggers on "aws cli help", "aws command line", "aws scripting", "aws automation", "aws batch operations", "aws bulk operations", "aws cli pagination", "aws multi-region", "aws profiles", "aws cli troubleshooting".

192 20
Explore
giuseppe-trisciuoglio/developer-kit

aws-cost-optimization

Provides structured AWS cost optimization guidance using five pillars (right-sizing, elasticity, pricing models, storage optimization, monitoring) and twelve actionable best practices with executable AWS CLI examples. Use when optimizing AWS costs, reviewing AWS spending, finding unused AWS resources, implementing FinOps practices, reducing EC2/EBS/S3 bills, configuring AWS Budgets, or performing AWS Well-Architected cost reviews.

192 20
Explore
giuseppe-trisciuoglio/developer-kit

aws-sam-bootstrap

Provides AWS SAM bootstrap patterns: generates `template.yaml` and `samconfig.toml` for new projects via `sam init`, creates SAM templates for existing Lambda/CloudFormation code migration, validates build/package/deploy workflows, and configures local testing with `sam local invoke`. Use when the user asks about SAM projects, `sam init`, `sam deploy`, serverless deployments, or needs to bootstrap/migrate Lambda functions with SAM templates.

192 20
Explore
giuseppe-trisciuoglio/developer-kit

aws-drawio-architecture-diagrams

Creates professional AWS architecture diagrams in draw.io XML format (.drawio files) using official AWS Architecture Icons (aws4 library). Use when the user asks for AWS diagrams, VPC layouts, multi-tier architectures, serverless designs, network topology, or draw.io exports involving Lambda, EC2, RDS, or other AWS services.

192 20
Explore
giuseppe-trisciuoglio/developer-kit

aws-cloudformation-bedrock

Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation guardrails, managing prompts, orchestrating workflows with flows, and configuring inference profiles for model optimization.

192 20
Explore
giuseppe-trisciuoglio/developer-kit

aws-cloudformation-s3

Provides AWS CloudFormation patterns for Amazon S3. Use when creating S3 buckets, policies, versioning, lifecycle rules, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.

192 20
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results