EasyRAG: Efficient Retrieval-Augmented Generation Framework for Automated Network Operations

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 implementation. The last one is efficient inference. We designed an efficient inference acceleration scheme for the entire coarse ranking, reranking, and generation process that significantly reduces the inference latency of RAG while maintaining a good level of accuracy; each acceleration scheme can be plug-and-play into any component of the RAG process, consistently enhancing the efficiency of the RAG system. Our code and data are released at \url{https://github.com/BUAADreamer/EasyRAG}.