qdrant

Build vector retrieval into Java applications.

semantic search

What it does

Guides integration of Qdrant vector storage and similarity retrieval into Java applications using Spring Boot and LangChain4j.

When to use it

Use for semantic search, recommendation systems, RAG pipelines, and filtered similarity search in Java applications.

Give it a Java project; it provides Qdrant integration patterns for embedding storage, similarity search, and vector management.

What you provide

This skill

Qdrant

Write

Creates collections and upserts vectors

qdrant/qdrant

Execute

Starts a local Qdrant container

Docker is required to deploy the instructed local Qdrant container.

The Java integration adds Qdrant client version 1.15.0 through Maven or Gradle.

The optional RAG example uses the LangChain4j Qdrant integration package.

The optional advanced RAG example uses LangChain4j's OpenAI integration.