Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems

TL;DR

This paper introduces ES-RAG, a retrieval-augmented generation framework utilizing Elasticsearch, which improves question-answering accuracy and efficiency over traditional methods in large-scale language models.

Contribution

The study proposes ES-RAG, a novel retrieval method integrating Elasticsearch into RAG, enhancing retrieval speed and accuracy for question-answering systems.

Findings

ES-RAG outperforms TF-IDF-RAG in accuracy by 0.51 percentage points.

Elasticsearch improves retrieval efficiency and handles complex queries better.

The system offers more flexible and efficient responses based on user needs.

Abstract

This study aims to improve the accuracy and quality of large-scale language models (LLMs) in answering questions by integrating Elasticsearch into the Retrieval Augmented Generation (RAG) framework. The experiment uses the Stanford Question Answering Dataset (SQuAD) version 2.0 as the test dataset and compares the performance of different retrieval methods, including traditional methods based on keyword matching or semantic similarity calculation, BM25-RAG and TF-IDF- RAG, and the newly proposed ES-RAG scheme. The results show that ES-RAG not only has obvious advantages in retrieval efficiency but also performs well in key indicators such as accuracy, which is 0.51 percentage points higher than TF-IDF-RAG. In addition, Elasticsearch's powerful search capabilities and rich configuration options enable the entire question-answering system to better handle complex queries and provide more flexible and efficient responses based on the diverse needs of users. Future research directions can further explore how to optimize the interaction mechanism between Elasticsearch and LLM, such as introducing higher-level semantic understanding and context-awareness capabilities, to achieve a more intelligent and humanized question-answering experience.