评测检索增强生成中不同检索与提示策略的组合效果
RAG Playground: A Framework for Systematic Evaluation of Retrieval Strategies and Prompt Engineering in RAG Systems
- 对比向量搜索、重排序和混合检索，结合不同提示策略评估
- 混合检索+结构化提示使多指标通过率最高达72.7%
- 适合关注RAG系统优化的研究者与工程实践者
我们提出RAG Playground，一个开源框架，用于系统性评估检索增强生成（RAG）系统。该框架实现并比较了三种检索方法：朴素向量搜索、重排序和混合向量-关键词搜索，并结合使用不同提示策略的ReAct代理。引入全新的评估体系与指标，对Llama 3.1和Qwen 2.5两种语言模型在多种检索配置下的表现进行实证分析。实验表明，混合搜索方法和结构化自评估提示可显著提升性能，多指标评估框架下最高达到72.7%的通过率。结果也凸显了提示工程在RAG中的关键作用，定制提示的代理在检索准确率和响应质量上均有稳定提升。
原文摘要 · Abstract (English)
We present RAG Playground, an open-source framework for systematic evaluation of Retrieval-Augmented Generation (RAG) systems. The framework implements and compares three retrieval approaches: naive vector search, reranking, and hybrid vector-keyword search, combined with ReAct agents using different prompting strategies. We introduce a comprehensive evaluation framework with novel metrics and provide empirical results comparing different language models (Llama 3.1 and Qwen 2.5) across various retrieval configurations. Our experiments demonstrate significant performance improvements through hybrid search methods and structured self-evaluation prompting, achieving up to 72.7% pass rate on our multi-metric evaluation framework. The results also highlight the importance of prompt engineering in RAG systems, with our custom-prompted agents showing consistent improvements in retrieval accuracy and response quality.
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