Qdrant is a vector database & vector similarity search engine. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!
- Easy to Use APIProvides the OpenAPI v3 specification to generate a client library in almost any programming language. Alternatively utilise ready-made client for Python or other programming languages with additional functionality.
- Fast and AccurateImplement a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. Search with a State-of-the-Art speed and apply search filters without compromising on results.
- FilterableSupport additional payload associated with vectors. Not only stores payload but also allows filter results based on payload values.
- Unlike Elasticsearch post-filtering, Qdrant guarantees all relevant vectors are retrieved.Rich data types
- Vector payload supports a large variety of data types and query conditions, including string matching, numerical ranges, geo-locations, and more. Payload filtering conditions allow you to build almost any custom business logic that should work on top of similarity matching.
- Distributed Cloud-native and scales horizontally.No matter how much data you need to serve - Qdrant can always be used with just the right amount of computational resources.
- EfficientEffectively utilizes your resources. Developed entirely in Rust language, Qdrant implements dynamic query planning and payload data indexing. Hardware-aware builds are also available for Enterprises.
