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Qdrant is an open-source vector search engine. Qdrant deploys as an API service for searching 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 API
Qdrant provides the OpenAPI v3 specification, which allows you to generate a client library in almost any programming language. Or you can use ready-made clients for popular programming languages with additional functionality.
Fast and Accurate
https://github.com/erikbern/ann-benchmarks https://blog.vasnetsov.com/posts/categorical-hnsw/
Qdrant implements a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. (TBD) This algorithm allows Qdrant to search with a State-of-the-Art speed and apply search filters without losing the results.
Filtrable
Qdrant supports additional payload associated with vectors. Qdrant not only stores payload but also allows filter results based on payload values. Unlike Elasticsearch k-NN search, Qdrant does not perform post-filtering, so it guarantees that all relevant vectors will be 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
Qdrant is 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. (For now - enterprise only)
Optimized
We made certain Qdrant makes the most of the resources provided. Among the optimizations used:
- Engine built entirely in Rust language
- Dynamic query planning
- Payload data indexing
- Hardware-aware builds (Enterprise only)