Qdrant is a vector database and vector search engine designed for deploying AI applications that rely on similarity search. Written in Rust, it provides a high-performance, scalable solution for storing and querying high-dimensional vectors. Key features include:
- Similarity Search: Efficiently finds the nearest vectors based on various distance metrics.
- Scalability: Designed to handle billions of vectors with low latency.
- Filtering: Supports filtering vectors based on associated payload data.
- Rust-based: Ensures reliability and performance.
- Cloud Native: Offers managed cloud solutions with scalability and high availability.
- Quantization: Reduces memory usage with built-in compression options.
Use cases include advanced search, recommendation systems, retrieval-augmented generation (RAG), data analysis, anomaly detection, and AI agents. It integrates with leading embedding models and frameworks, making it suitable for a wide range of AI applications.
