The QdrantNeo4jRetriever package integrates Neo4j graph databases with Qdrant vector search databases, enabling the use of both for retrieval-augmented generation (RAG) pipelines. This setup allows users to leverage Neo4j's graph model to enrich AI queries with contextual relationships while offloading embeddings to Qdrant for high-performance similarity searches. The package supports various vector databases and provides a simple way to connect these systems, making it easier to explore how RAG workflows can be enhanced by combining graph data and vector similarity searches. By using the QdrantNeo4jRetriever, users can quickly set up a local environment to test this integration and start exploring its potential in AI projects.