用Elasticsearch优化RAG，让问答系统更准更快。
Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems
- 将Elasticsearch融入RAG框架，提升检索效率与准确性。
- 在SQuAD 2.0上，准确率比TF-IDF-RAG高0.51个百分点。
- 适合需要高效处理复杂查询的智能问答系统开发者。
本研究旨在通过将Elasticsearch集成到检索增强生成（RAG）框架中，提升大规模语言模型在问答任务中的准确性和质量。实验采用SQuAD 2.0数据集作为测试基准，对比了基于关键词匹配或语义相似度计算的传统检索方法（如BM25-RAG、TF-IDF-RAG）与新提出的ES-RAG方案。结果表明，ES-RAG不仅在检索效率上表现突出，且在关键指标上优于现有方法，准确率较TF-IDF-RAG高出0.51个百分点。此外，Elasticsearch强大的搜索能力与丰富的配置选项，使整个问答系统能更灵活高效地应对复杂查询，满足用户多样化需求。未来可进一步探索Elasticsearch与大模型间交互机制的优化，如引入更高层次的语义理解与上下文感知能力，实现更智能、人性化的问答体验。
原文摘要 · Abstract (English)
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.
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