qdrant-vector-search

Vector search for production RAG pipelines is enabled through a Rust-powered backend that provides fast nearest-neighbor search and scalable storage for large embedding datasets. You use the qdrant-client Python library to index and query your knowledge base with low latency.

Hybrid search with metadata filtering is fully supported in Qdrant, allowing you to combine vector similarity search with payload filtering. This enables precise retrieval by applying metadata conditions alongside multi-vector storage during your queries.

Distributed deployment is supported in Qdrant, featuring multi-node redundancy and horizontal scaling across clusters. This allows your vector search engine to handle large-scale embedding datasets with on-disk payloads for production-grade reliability.

Low latency nearest-neighbor search for embeddings is achieved using Qdrant's Rust-powered backend, which delivers production-grade performance. It exposes REST and gRPC APIs for fast retrieval and scalable storage within RAG workflows.

You need the qdrant-client Python library, version 1.12.0 or higher, to perform semantic search and interact with the Qdrant backend. It provides the integration needed for indexing embeddings and querying your vector database.

On-disk payload storage should be used when scaling vector search to embedding datasets that exceed available RAM. Qdrant supports on-disk payloads alongside multi-vector storage to maintain scalable and cost-effective production deployments.