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 ```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(...