AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This paper proposes an enhanced GraphRAG framework that integrates a transformer-based Multi-Hop Knowledge Graph Completion (KGC) model directly into the retrieval pipeline, and provides substantial gains in answer quality and reasoning capability for queries involving indirect dependencies not explicitly encoded in the original graph.
RAGU, an open-source modular GraphRAG engine, addresses extraction from consolidation by separating extraction from consolidation: entities and relations pass through two-stage typed extraction, DBSCAN-backed deduplication, LLM summarization, and Leiden community detection.
Integrating Knowledge Graphs (KGs) into Retrieval-Augmented Generation (RAG) can substantially improve LLM performance on complex question answering (QA) by reducing hallucinations and supplying structured context. However, building high-quality KGs over large corpora for edge scenarios is challenging: cloud-based proc...
Experiments show that DuPLeR achieves robust performance in data-scarce KGC scenarios, and a dual-pathway multimodal enhancement module regulates message passing with query-relevant multimodal signals and supplements entity representations after graph propagation.
SelfGraphRAG, a framework that generates question-answer pairs directly from knowledge graph structure and uses them to train a query-conditioned graph retriever, suggests that knowledge graph structure can provide useful supervision for training graph retrievers when labeled data are unavailable.
This work introduces a GLM-based retriever and investigates the comparative strengths of GLM-based, GNN-based, and traditional vector-search-based retrievers in single- and multi-hop RAG settings, and suggests that finetuned GLM retrievers generalize better out of domain.
Experimental results show consistent gains in answer quality and evidence faithfulness over strong multimodal RAG, graph-retrieval, and open-source vision-language baselines, with improved retrieval quality on MultiModalQA and stable performance under noisy evidence pools.
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