RAGdb: A Zero-Dependency, Embeddable Architecture for Multimodal Retrieval-Augmented Generation on the Edge
#Retrieval-Augmented Generation #Edge AI #Vector Search #Zero-Dependency Architecture #Knowledge Container #Multimodal Processing #Green AI #Local-First AI
📌 Key Takeaways
- Ahmed Bin Khalid developed RAGdb, a zero-dependency architecture for edge-based multimodal RAG
- Current RAG systems suffer from 'infrastructure bloat' with complex, distributed requirements
- RAGdb achieves 100% Recall@1 for entity retrieval and 31.6x efficiency gain in incremental updates
- The system reduces disk footprint by 99.5% compared to standard Docker-based RAG stacks
- RAGdb enables decentralized AI applications with local-first knowledge containers
📖 Full Retelling
Researcher Ahmed Bin Khalid introduced RAGdb, a novel zero-dependency architecture for multimodal retrieval-augmented generation on edge devices, through a paper submitted to arXiv on December 9, 2025, addressing the significant infrastructure bloat in current RAG systems that creates barriers for edge computing and privacy-constrained applications. The paper highlights how Retrieval-Augmented Generation has become the standard paradigm for grounding Large Language Models in domain-specific data, but current architectures have evolved into complex distributed stacks requiring cloud-hosted vector databases, heavy deep learning frameworks like PyTorch and CUDA, and high-latency embedding inference servers. This complexity creates substantial barriers for edge computing environments, air-gapped systems, and applications where data sovereignty is paramount. RAGdb presents an innovative solution as a monolithic architecture that consolidates automated multimodal ingestion, ONNX-based extraction, and hybrid vector retrieval into a single, portable SQLite container. The system implements a deterministic Hybrid Scoring Function that combines sublinear TF-IDF vectorization with exact substring boosting, eliminating the need for GPU inference at query time. Experimental evaluations conducted on an Intel i7-1165G7 consumer laptop demonstrated impressive results, including 100% Recall@1 for entity retrieval and an ingestion efficiency gain of 31.6x during incremental updates compared to cold starts. Furthermore, RAGdb reduces disk footprint by approximately 99.5% compared to standard Docker-based RAG stacks, establishing the 'Single-File Knowledge Container' as a viable primitive for decentralized, local-first AI applications.
🏷️ Themes
Edge Computing, AI Architecture, Privacy-Preserving Technology
📚 Related People & Topics
Edge computing
Distributed computing paradigm
Edge computing is a distributed computing model that brings computation and data storage closer to the sources of data. More broadly, it refers to any design that pushes computation physically closer to a user, so as to reduce the latency compared to when an application runs on a centralized data ce...
Entity Intersection Graph
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Original Source
--> Computer Science > Information Retrieval arXiv:2602.22217 [Submitted on 9 Dec 2025] Title: RAGdb: A Zero-Dependency, Embeddable Architecture for Multimodal Retrieval-Augmented Generation on the Edge Authors: Ahmed Bin Khalid View a PDF of the paper titled RAGdb: A Zero-Dependency, Embeddable Architecture for Multimodal Retrieval-Augmented Generation on the Edge, by Ahmed Bin Khalid View PDF HTML Abstract: Retrieval-Augmented Generation has established itself as the standard paradigm for grounding Large Language Models in domain-specific, up-to-date data. However, the prevailing architecture for RAG has evolved into a complex, distributed stack requiring cloud-hosted vector databases, heavy deep learning frameworks (e.g., PyTorch, CUDA), and high-latency embedding inference servers. This ``infrastructure bloat'' creates a significant barrier to entry for edge computing, air-gapped environments, and privacy-constrained applications where data sovereignty is paramount. This paper introduces RAGdb, a novel monolithic architecture that consolidates automated multimodal ingestion, ONNX-based extraction, and hybrid vector retrieval into a single, portable SQLite container. We propose a deterministic Hybrid Scoring Function that combines sublinear TF-IDF vectorization with exact substring boosting, eliminating the need for GPU inference at query time. Experimental evaluation on an Intel i7-1165G7 consumer laptop demonstrates that RAGdb achieves 100\% Recall@1 for entity retrieval and an ingestion efficiency gain of 31.6x during incremental updates compared to cold starts. Furthermore, the system reduces disk footprint by approximately 99.5\% compared to standard Docker-based RAG stacks, establishing the ``Single-File Knowledge Container'' as a viable primitive for decentralized, local-first AI. Keywords: Edge AI, Retrieval-Augmented Generation, Vector Search, Green AI, Serverless Architecture, Knowledge Graphs, Efficient Computing. Comments: 6 pages, 2 tables Subjects: ...
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