Our approach to vector data
Last updated
Last updated
We provide developers with a tool for building autonomous AI agents that boasts fault tolerance, enhanced reliability, security, and high responsiveness under heavy load conditions.
All this is achieved through decentralization and cryptography.
- Indexing: VES utilizes decentralized data structures, such as Distributed Hash Tables (DHT), for indexing vector embeddings. This provides fast access and resilience to failures.
- Storage: Data is stored in a decentralized network, minimizing risks associated with centralized data storage and making the system more resistant to attacks and technical failures.
- The use of homomorphic encryption and zero-knowledge proofs allows VES to process similarity search queries and other operations without disclosing the contents of the vectors, which is critical for maintaining the confidentiality of user data.
VES provides an API that offers advanced semantic search capabilities. Users can efficiently find vector embeddings based on semantic proximity using complex search algorithms such as k-nearest neighbors (k-NN).
- The system is designed for horizontal scaling, allowing easy addition of processing and storage nodes as data volumes grow.
- Serverless architecture reduces overhead costs associated with managing and supporting infrastructure, allowing users to focus on analytics and data interaction.
- The use of smart contracts to manage access and transactions within VES automates many processes and provides users with flexible and transparent mechanisms for interacting with the system.
- Tokenization of actions and operations in the system motivates users to actively participate and promotes a fairer distribution of resources.
