Qdrant

Qdrant is a powerful vector database and similarity search engine that enables developers to build AI applications for matching, searching, recommending, and more. It offers features such as filterable payload support, rich data types, and distributed scalability.

Key Features:

1. Filterable: Qdrant allows the storage of additional payload associated with vectors and enables filtering results based on payload values.

2. Rich Data Types: The vector payload in Qdrant supports various data types and query conditions like string matching, numerical ranges, geo-locations, etc., allowing for custom business logic implementation.

3. Distributed & Efficient: Qdrant is cloud-native and scales horizontally while efficiently utilizing computational resources. It implements dynamic query planning and hardware-aware builds for optimal performance.

Use Cases:

1. Similar Image Search: With Qdrant's vector database capabilities, users can find similar images by detecting duplicates or even searching for pictures based on text descriptions.

2. Custom Business Logic: Qdrant's filter functionality enables the application of arbitrary business logic on top of similarity searches. For example, finding clothes cheaper than $20 or artwork published in the last year.

3. Food Discovery Service: Using Qdrant's demo food discovery service as an example use case showcases its ability to suggest lunch options based on visual preferences or search for nearby places.

Conclusion:

Qdrant provides developers with a robust solution for building AI applications that require efficient vector similarity search capabilities. Its features like filterable payloads, support for rich data types, distributed scalability ensure high performance while handling complex tasks like image similarity search or implementing custom business logic on top of similarity matching operations.