ZeroRAG Introduces Decentralized Retrieval-Augmented Generation System for Data Ownership and Scalability
ZeroRAG has developed a new decentralized retrieval-augmented generation (RAG) system designed to achieve high recall and accuracy without relying on a centralized document store or global index. This system allows for efficient data retrieval by routing queries to find data where it already resides, rather than replicating data across nodes. This approach addresses challenges related to data ownership and availability, particularly in scenarios where data replication is not permissible, such as in healthcare. ZeroRAG utilizes similarity search over high-dimensional embedding spaces, where documents are encoded by pre-trained machine learning models to capture semantic relationships. Queries are embedded in the same space, and the system returns the nearest neighbors using vector distance, accelerated by Approximate Nearest Neighbor (ANN) indexing techniques for large datasets. The system is built on a permissioned decentralized overlay network that reconfigures connections between peers as queries are iss...
