surrealdb

Hybrid RAG with SurrealDB unifies semantic search, graph traversal, and timeline queries into a single data layer. This multi-model approach retrieves contextual data across entity relationships and chronological events, improving reasoning for complex knowledge bases.

You combine vector search and graph traversal in SurrealQL by applying MTREE vector indexes for semantic similarity and using native graph patterns for relationship modeling. Ready-to-use pattern files accelerate implementing this hybrid retrieval architecture.

Yes, SurrealDB supports MTREE vector indexes for semantic similarity search. It includes dimension-aware embedding handling to manage vector data efficiently alongside graph and timeline queries within a multi-model schema.

Yes, you can use SurrealDB timeline queries to maintain chronological coherence. The system provides timeline-based sequencing and event queries to order narrative data, guiding the writing and editing process for long-form projects.

The best way to structure this schema is using a multi-model architecture in SurrealDB. It accommodates MTREE vector indexes, SurrealQL graph relationships, and event timelines concurrently, enabling end-to-end retrieval across all three dimensions.

Yes, ready-to-use query pattern files are available, including semantic.surql, graph.surql, and timeline.surql. These files document the workflow and accelerate development of hybrid semantic-graph-timeline retrieval systems.