qdrant-vector-search
Rust-powered vector database for production RAG with hybrid search and distributed scaling.
- 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
- Rich filtering during search across any payload field, with optional payload indexing for performance and support for complex boolean queries
- Quantization options (scalar, product, binary) and on-disk storage for memory efficiency in large-scale deployments
- Integrates with sentence-transformers, LangChain, and LlamaIndex for RAG pipelines; includes batch search, REST and gRPC APIs, and distributed deployment with sharding and replication
Qdrant - Vector Similarity Search Engine
High-performance vector database written in Rust for production RAG and semantic search.
When to use Qdrant
Use Qdrant when:
- Building production RAG systems requiring low latency
- Need hybrid search (vectors + metadata filtering)
- Require horizontal scaling with sharding/replication
- Want on-premise deployment with full data control
- Need multi-vector storage per record (dense + sparse)
- Building real-time recommendation systems
Key features:
- Rust-powered: Memory-safe, high performance
- Rich filtering: Filter by any payload field during search
- Multiple vectors: Dense, sparse, multi-dense per point
- Quantization: Scalar, product, binary for memory efficiency
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