RAGdb让AI在本地用单文件就能高效检索多模态知识，无需云端和显卡。
RAGdb: A Zero-Dependency, Embeddable Architecture for Multimodal Retrieval-Augmented Generation on the Edge
- 把数据处理、向量提取、检索全塞进一个SQLite文件，零依赖运行。
- 在普通笔记本上实现100%召回率，增量更新速度提升31.6倍。
- 适合离线环境、隐私敏感场景，是轻量级本地AI的实用方案。
检索增强生成（RAG）已成为将大语言模型与领域特定、实时数据结合的标准范式。然而，当前RAG架构演变为复杂的分布式系统，依赖云端向量数据库、深度学习框架（如PyTorch、CUDA）及高延迟的嵌入推理服务，导致基础设施臃肿，严重阻碍了边缘计算、隔离环境及数据主权要求高的应用场景。本文提出RAGdb，一种新型单体架构，将自动化多模态数据摄入、基于ONNX的特征提取与混合向量检索整合于单一可移植的SQLite容器中。我们设计了一种确定性的混合评分函数（HSF），结合次线性TF-IDF向量化与精确子串匹配增强，避免查询时的GPU推理。在配备Intel i7-1165G7处理器的消费级笔记本上测试表明，RAGdb实现了实体检索100%的Recall@1，增量更新的吞吐效率较冷启动提升31.6倍。此外，相比标准Docker化RAG架构，系统磁盘占用减少约99.5%，确立了“单文件知识容器”作为去中心化、本地优先AI的可行基础构件。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) has established itself as the standard paradigm for grounding Large Language Models (LLMs) 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 (HSF) 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.
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