qdrant-vector-search

Hybrid search with metadata filters combines dense and sparse embeddings to refine retrieval results. This engine supports multi-vector storage and filtering, enabling fast nearest-neighbor search for real-time document retrieval in production-grade RAG systems.

Low-latency vector search on large datasets is achieved using a Rust-powered engine. It provides fast nearest-neighbor retrieval, multi-vector support for dense and sparse embeddings, and distributed deployment options for scalable enterprise knowledge base search.

Yes, the vector search engine supports both REST and gRPC APIs alongside multi-vector storage. This allows you to manage dense and sparse embeddings while utilizing hybrid search with metadata filters for enterprise document retrieval.

Yes, you can build a production-grade knowledge base search for enterprise documents. It provides fast retrieval, hybrid search with metadata filters for implementing access controls, and scalable vector storage to manage large datasets efficiently.