StreamingRAG: Real-time Contextual Retrieval and Generation Framework

• URL: http://arxiv.org/abs/2501.14101v1
• Date: Thu, 23 Jan 2025 21:20:10 GMT
• Title: StreamingRAG: Real-time Contextual Retrieval and Generation Framework
• Authors: Murugan Sankaradas, Ravi K. Rajendran, Srimat T. Chakradhar,
• Abstract summary: StreamingRAG is a novel framework designed for streaming data analysis.<n>It achieves significant improvements in real-time analysis (5-6x faster throughput), contextual accuracy (through a temporal knowledge graph), and reduced resource consumption (using lightweight models by 2-3x)
• Score: 0.8309949345495992
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Extracting real-time insights from multi-modal data streams from various domains such as healthcare, intelligent transportation, and satellite remote sensing remains a challenge. High computational demands and limited knowledge scope restrict the applicability of Multi-Modal Large Language Models (MM-LLMs) on these data streams. Traditional Retrieval-Augmented Generation (RAG) systems address knowledge limitations of these models, but suffer from slow preprocessing, making them unsuitable for real-time analysis. We propose StreamingRAG, a novel RAG framework designed for streaming data. StreamingRAG constructs evolving knowledge graphs capturing scene-object-entity relationships in real-time. The knowledge graph achieves temporal-aware scene representations using MM-LLMs and enables timely responses for specific events or user queries. StreamingRAG addresses limitations in existing methods, achieving significant improvements in real-time analysis (5-6x faster throughput), contextual accuracy (through a temporal knowledge graph), and reduced resource consumption (using lightweight models by 2-3x).

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