qdrant▌

This skill covers using Qdrant as the storage backend for GrepAI, offering high-performance vector search.

Rust-powered vector database for production RAG with hybrid search and distributed scaling. \n \n Supports dense, sparse, and multi-vector storage per point with four distance metrics (COSINE, EUCLID, DOT, MANHATTAN) and HNSW indexing for fast nearest-neighbor search \n Rich filtering during search across any payload field, with optional payload indexing for performance and support for complex boolean queries \n Quantization options (scalar, product, binary) and on-disk storage for memory effici

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.