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.
npx skills add majiayu000/claude-skill-registry --skill qdrant-giuseppe-trisciuogli-developer-kit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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.newBuild
- Overview
- When to Use
- Instructions
- 1. Deploy Qdrant with Docker
- 2. Add Dependencies
- 3. Initialize Client
- 4. Create Collection
- 5. Upsert Vectors
- 6. Search Vectors
- LangChain4j Integration
- Spring Boot Integration
- Examples
- REST Search Endpoint
- Best Practices
docker run -p 6333:6333 -p 6334:6334 \ qdrant/qdrant
What does the qdrant skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill qdrant-giuseppe-trisciuogli-developer-kit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.