RAGdb: 一种无依赖、可嵌入的多模态检索增强生成边缘计算架构
RAGdb: A Zero-Dependency, Embeddable Architecture for Multimodal Retrieval-Augmented Generation on the Edge
AI总结：
RAGdb通过单文件SQLite容器实现多模态检索增强生成，无需GPU推理，提升边缘计算效率与数据主权
摘要：
检索增强生成（RAG）已建立为将大型语言模型（LLMs）与领域特定、最新数据相结合的标准范式。然而，现有的RAG架构已演变为复杂的分布式堆栈，需要云托管的向量数据库、重型深度学习框架（例如PyTorch、CUDA）和高延迟的嵌入推理服务器。这种"基础设施膨胀"在边缘计算、隔离环境和受隐私约束的应用中形成了显著的进入障碍，其中数据主权至关重要。本文介绍了RAGdb，一种新的单体架构，将自动化多模态摄入、基于ONNX的提取和混合向量检索整合到单一、便携的SQLite容器中。我们提出了一种确定性的混合评分函数（HSF），结合了次线性TF-IDF向量化和精确子字符串增强，消除了查询时对GPU推理的需要。在Intel i7-1165G7消费级笔记本电脑上的实验评估显示，RAGdb在实体检索中实现了100% Recall@1，并且在增量更新期间相比冷启动实现了31.6倍的摄入效率提升。此外，该系统将磁盘占用量减少了约99.5%，确立了"单文件知识容器"作为去中心化、本地优先AI的可行原始构件。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
