EasyRAG: Efficient Retrieval-Augmented Generation Framework for Automated Network Operations
Zhangchi Feng, Dongdong Kuang, Zhongyuan Wang, Zhijie Nie, Yaowei, Zheng, Richong Zhang

TL;DR
EasyRAG is a lightweight, efficient retrieval-augmented generation framework for automated network operations, combining simple retrieval and reranking methods with optimized inference to achieve high accuracy and scalability.
Contribution
The paper introduces EasyRAG, a novel RAG framework that is simple to deploy, requires no model fine-tuning, and offers significant inference speed improvements for network automation tasks.
Findings
Achieved top placement in GLM4 track competitions
Requires no model fine-tuning, reducing deployment complexity
Significantly reduces inference latency while maintaining accuracy
Abstract
This paper presents EasyRAG, a simple, lightweight, and efficient retrieval-augmented generation framework for automated network operations. Our framework has three advantages. The first is accurate question answering. We designed a straightforward RAG scheme based on (1) a specific data processing workflow (2) dual-route sparse retrieval for coarse ranking (3) LLM Reranker for reranking (4) LLM answer generation and optimization. This approach achieved first place in the GLM4 track in the preliminary round and second place in the GLM4 track in the semifinals. The second is simple deployment. Our method primarily consists of BM25 retrieval and BGE-reranker reranking, requiring no fine-tuning of any models, occupying minimal VRAM, easy to deploy, and highly scalable; we provide a flexible code library with various search and generation strategies, facilitating custom process…
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Code & Models
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Taxonomy
TopicsDistributed and Parallel Computing Systems · Mobile Agent-Based Network Management · Network Packet Processing and Optimization
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Linear Layer · Multi-Head Attention · Dense Connections · WordPiece · Residual Connection · Linear Warmup With Linear Decay · Dropout · Layer Normalization · Adam
