Introduction
Qdrant is a production-grade vector search engine and vector database that provides high-performance similarity search, persistent storage, and scalable deployment capabilities. It improves query efficiency through quantization, indexing, and filtering mechanisms, and offers unified API support for multi-language clients and cloud-hosted services.
Key Features
- High-performance vector search with quantization support, balancing throughput and latency.
- Flexible payload filtering and query expressions for complex condition screening.
- Rich client libraries and OpenAPI interfaces for easy integration with various languages and frameworks.
- Managed Qdrant Cloud and self-hosted deployment options.
Use Cases
- Semantic search and RAG retrieval: Perform similarity search and recall on text, images, or multimodal data.
- Recommendation systems and personalized ranking: Achieve approximate recommendations based on vector similarity and attribute filtering.
- Large-scale offline/online hybrid queries: Applications requiring low-latency retrieval and scalable storage.
Technical Highlights
- Implemented in Rust with a focus on performance and stability, supporting distributed deployment and horizontal scaling.
- Provides indexing (such as HNSW), quantization, and persistence strategies, along with multiple client libraries and backup/restore mechanisms.