In this project, I developed a cutting-edge platform that offers real-time Approximate Nearest Neighbor (ANN) search capabilities as a service. The platform is designed to handle large-scale vector spaces and provide efficient and scalable nearest neighbor search functionality.

Key features and capabilities of the platform include:

Dynamic Element Management: The platform supports seamless addition and deletion of vector elements within the underlying data structure. This allows for efficient updates and maintenance of the vector space without disrupting ongoing operations.

Vector Space Onboarding: The platform provides a streamlined process for onboarding new vector spaces. Users can easily integrate their high-dimensional data, enabling the platform to perform nearest neighbor searches across diverse datasets and applications.

Recall vs. Latency Optimization: The platform employs advanced techniques to manage and optimize the trade-off between recall (the accuracy of the nearest neighbor search results) and latency (the response time). This ensures that users can strike the right balance between precision and performance based on their specific requirements.

High Scalability: Designed for high-throughput scenarios, the platform can handle over 10,000 requests per second (RPS) while maintaining low latency and efficient resource utilization. This scalability is achieved through a combination of optimized data structures, efficient algorithms, and horizontal scaling capabilities.