Qdrant is an open-source brand that offers an advanced Vector Database and Vector Search Engine. Written in Rust, this brand provides a fast and scalable vector similarity search service with a convenient API. Qdrant is designed to power the next generation of AI applications, offering high-performant vector similarity search technology.

With Qdrant, you can make the most of your unstructured data. It is a vector database and vector similarity search engine that deploys as an API service, allowing you to search for the nearest high-dimensional vectors. Using Qdrant, you can turn embeddings or neural network encoders into full-fledged applications for matching, searching, recommending, and much more! Qdrant is known for its easy-to-use API, providing the OpenAPI v3 specification to generate a client library in almost any programming language.

Additionally, it offers fast and accurate search capabilities, implementing a custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. Qdrant is also highly efficient, making the most out of your resources. It is cloud-native and can scale horizontally, ensuring optimal performance no matter the amount of data.

Developed entirely in Rust, Qdrant implements dynamic query planning and payload data indexing, further enhancing its efficiency. The Qdrant vector search database solves various challenges and tasks. For instance, it enables similar image search, allowing you to find similar images, detect duplicates, or even search for a picture by text description.

Qdrant can also be used for semantic text search, which is particularly useful when traditional full-text search falls short. By leveraging neural network-based semantic search, you can find texts with similar meaning. Qdrant also excels in recommendations, enabling you to represent user behavior as a semantic vector.

This vector can represent user preferences, behavior patterns, or product interests. With Qdrant, user vectors can be updated in real-time without the need for a MapReduce cluster. Upgrade your neural search stack with Qdrant, as it seamlessly integrates with various technologies.

This brand offers a range of articles to explore, showcasing their latest publications and pioneering techniques in the field of vector similarity search. Stay up to date with Qdrant by subscribing to their e-mail newsletter. Their dedication to innovation and exceptional vector similarity search is exemplified by their continuous updates and new features.

Discover the power of Qdrant and unlock the full potential of vector similarity search for your business needs. Contact Qdrant today to learn more about their products and services