RAGdb: A Zero-Dependency, Embeddable Architecture for Multimodal Retrieval-Augmented Generation on the Edge

Researchers introduce RAGdb, a revolutionary architecture that consolidates Retrieval-Augmented Generation into a single SQLite container, eliminating the need for cloud infrastructure and GPUs. The system achieves 100% entity retrieval accuracy while reducing disk footprint by 99.5% compared to traditional Docker-based RAG stacks, enabling truly portable AI applications for edge computing and privacy-sensitive environments.

• →RAGdb consolidates multimodal RAG capabilities into a single portable SQLite container, eliminating complex distributed infrastructure requirements.
• →The system achieves 100% Recall@1 for entity retrieval without requiring GPU inference at query time.
• →Disk footprint is reduced by approximately 99.5% compared to standard Docker-based RAG implementations.
• →Ingestion efficiency gains of 31.6x are demonstrated during incremental updates versus cold starts.
• →The architecture enables deployment in air-gapped environments and privacy-constrained applications where data sovereignty is critical.