qdrant-vector-search

To build a production RAG pipeline with low latency vector search, use a Rust-powered engine supporting high-throughput similarity search, payload filtering, and horizontal scaling to ensure robust real-time retrieval.

Hybrid search in a vector database combines dense and sparse vectors for semantic matching. Payload filtering restricts search results based on metadata attributes, enabling precise multi-vector queries before or after nearest-neighbor retrieval.

Qdrant supports on-premise deployment and horizontal scaling for semantic search through distributed deployment options and payload indexing, handling large-scale production workloads while maintaining low latency.

To set up a vector collection and perform a similarity search, install the client, create a collection, upsert your dense or sparse vectors, and execute a search query. The engine supports quantization to optimize storage and retrieval speed.

The best way to reduce memory usage during nearest-neighbor search is applying quantization. This technique compresses dense and sparse vectors, lowering memory consumption while maintaining high-throughput search performance.