Wayback Project

It enables the execution of hybrid queries that combine graph-based relationship traversal, vector-based similarity matching, and temporal filtering. Users can retrieve information from large knowledge bases by simultaneously querying structural connections, semantic proximity, and historical state transitions within a single SurrealDB instance.

Database architects, knowledge engineers, and backend developers working with complex, multi-modal datasets benefit most. It is designed for professionals managing large-scale information repositories who require unified access to graph, vector, and time-series data without maintaining disparate storage systems.

The primary prerequisite is a functional SurrealDB environment configured to support vector indexing and graph-based schema definitions. Users must ensure their data is structured to accommodate relational links and that embedding vectors are generated and stored within the database records prior to query execution.