qdrant-vector-search

Vector similarity search enables fast nearest-neighbor retrieval of relevant embeddings for RAG and semantic search. It solves the problem of querying large collections with low latency while supporting scalable storage of high-dimensional data.

You can filter vector search results by payload fields using hybrid search. This approach applies payload filters directly during vector queries, allowing you to narrow down nearest-neighbor retrieval based on specific metadata attributes.

Yes, Qdrant supports distributed vector search through sharding and replication. This architecture allows you to scale horizontally across multiple nodes, maintaining low-latency retrieval for large embedding collections in production environments.

Using a Rust-powered vector search engine is the best way to achieve low-latency semantic search in production. Rust provides memory safety and high performance for fast nearest-neighbor retrieval on large vector collections.

Yes, you can integrate vector search using both REST and gRPC APIs with full feature parity. This allows you to choose the protocol that best fits your application architecture while maintaining access to all search and filtering capabilities.

You need on-disk payload storage when scaling vector collections beyond available RAM. This approach stores payload fields on disk while keeping vectors optimized for memory, enabling scalable storage for production workloads.