Advanced Retrieval-Augmented Generation in Multi-Turn Dialogues and Heterogeneous Multi-Cloud Computing Environments
Abstract
Traditional Retrieval-Augmented Generation (RAG) systems face significant limitations in multi-turn dialogue scenarios, failing to effectively capture and utilize dynamic conversational context. This thesis introduces DH-RAG (Dynamic Historical Context-Powered Retrieval-Augmented Generation), a novel method specifically designed for multi-turn dialogues that integrates static external knowledge with dynamic conversational context. DH-RAG comprises a History-Learning based Query Reconstruction Module and a Dynamic History Information Updating Module, supported by a Dynamic Historical Information Database employing Historical Query Clustering, Hierarchical Matching, and Chain of Thought Tracking strategies. Experimental evaluation demonstrates that DH-RAG significantly outperforms conventional methods across multiple benchmarks, achieving improvements ranging from 25% to 85% in response quality metrics. On domain-specific datasets, DH-RAG achieves BLEU scores of 4.10 and F1 scores of 27.83, representing 215.38% and 58.13% improvements over baseline methods. To validate the practical applicability of DH-RAG, we deploy and characterize the algorithm across a distributed academic computing infrastructure spanning Hong Kong, Shandong, and Jiangsu regions. Our validation framework demonstrates that DH-RAG maintains algorithmic effectiveness when deployed across heterogeneous GPU resources, preserving 88.9% of single-node efficiency while supporting distributed scaling. Performance…
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