qdrant

This skill packages practical integration patterns for connecting the Qdrant vector database to Java applications, with an emphasis on Spring Boot and LangChain4j. It addresses the recurring problem of wiring an AI-native vector store into a JVM stack: deploying Qdrant, managing collections, storing embeddings, and running similarity or filtered searches without having to piece the workflow together from scattered documentation.

The material walks through the full lifecycle. It shows how to launch Qdrant via Docker (exposing the REST endpoint on 6333 and gRPC on 6334), add the Maven or Gradle client dependency, and initialize the QdrantClient with an optional API key for production. From there it covers creating collections with a chosen distance metric and vector size, upserting points with payloads, and running nearest-neighbor and filtered queries. A LangChain4j section demonstrates the higher-level QdrantEmbeddingStore for RAG pipelines, and Spring Boot examples show bean configuration, a VectorSearchService, and a REST search controller. Each step includes lightweight validation checkpoints.

It targets Java backend and AI/ML engineers building semantic search, recommendation engines, or retrieval-augmented generation systems on Spring Boot. It suits teams that already work in the LangChain4j ecosystem and want proven, copy-ready patterns rather than starting from the raw client API.