StreamingRAG: Real-time Contextual Retrieval and Generation Framework
Murugan Sankaradas, Ravi K.Rajendran, Srimat T.Chakradhar

TL;DR
StreamingRAG is a real-time retrieval and generation framework that constructs evolving knowledge graphs from multi-modal data streams, significantly improving speed, accuracy, and resource efficiency for applications like healthcare and remote sensing.
Contribution
It introduces a novel streaming RAG framework that builds temporal knowledge graphs in real-time, enabling faster and more accurate multi-modal data analysis.
Findings
5-6x faster throughput in real-time analysis
Improved contextual accuracy with temporal knowledge graphs
Reduced resource consumption by 2-3x using lightweight models
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…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Data Management and Algorithms · Video Analysis and Summarization
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Attention Is All You Need · Layer Normalization · Dense Connections · Softmax · Linear Warmup With Linear Decay · Adam · Residual Connection · Dropout · Byte Pair Encoding
