RAGPerf: An End-to-End Benchmarking Framework for Retrieval-Augmented Generation Systems

Retrieval-augmented generation (RAG) systems have made significant advances in intelligent data processing in recent years. By combining large language models (LLMs) with external knowledge bases, RAG systems can incorporate up-to-date knowledge into content generation, providing accurate responses in fields such as scientific research, legal discovery, and financial analysis. However, as RAG systems become widely used, effectively evaluating their performance has become a pressing issue. Existing RAG benchmarks often focus solely on semantic metrics, such as retrieval precision and generation accuracy, while neglecting system efficiency and performance bottlenecks. This limitation hinders the optimization and deployment of RAG systems in real-world applications. Therefore, developing a benchmarking framework capable of end-to-end performance analysis is crucial for advancing RAG systems.

Deploying RAG systems involves multiple performance bottlenecks, including complex interactions between components such as embedding, indexing, retrieval, reranking, and generation. The configuration choices of these components directly affect the overall system performance and query quality. However, due to the lack of a unified benchmarking framework, developers find it challenging to study the impact of these configurations on performance in real-world deployment scenarios. Additionally, existing benchmarking tools often lack flexibility, preventing users from customizing RAG pipeline configurations. Thus, developing a modular, extensible, and end-to-end performance analysis framework is a pressing issue.

RAGPerf's core innovations lie in its modular design and flexible configuration capabilities. By decomposing the RAG workflow into independent components, RAGPerf allows users to independently configure each component for detailed performance analysis in different application scenarios. • RAGPerf supports various dataset types and embedding models, capable of simulating diverse query and update distributions. • RAGPerf integrates multiple vector databases, such as LanceDB, Milvus, Qdrant, Chroma, and Elasticsearch, allowing users to choose the most suitable database based on their needs. • RAGPerf provides automated performance metric collection, including end-to-end query throughput, host/GPU memory footprint, and CPU/GPU utilization. These innovations enable RAGPerf to capture the performance behavior of RAG systems under different configurations, providing users with data-driven decision-making tools.

The design and implementation of RAGPerf include the following key steps: • Modular Design: Decompose the RAG workflow into components such as embedding, indexing, retrieval, reranking, and generation, allowing users to independently configure each component. • Dataset Support: RAGPerf supports various dataset types, including text, PDF, code, and audio, capable of simulating diverse query and update distributions. • Vector Database Integration: RAGPerf supports multiple vector databases, such as LanceDB, Milvus, Qdrant, Chroma, and Elasticsearch, allowing users to choose the most suitable database based on their needs. • Performance Metric Collection: RAGPerf provides automated performance metric collection, including end-to-end query throughput, host/GPU memory footprint, and CPU/GPU utilization. • Experimental Validation: Validate RAGPerf's performance analysis capabilities through testing on various datasets and configurations.

RAGPerf's experimental design includes various datasets and configurations to validate its performance analysis capabilities. • Datasets: The experiments use various datasets, including text, PDF, code, and audio, simulating diverse query and update distributions. • Baselines: The experiments compare RAGPerf with existing RAG benchmarking tools, evaluating its performance metric collection and system efficiency. • Metrics: The experiments collect performance metrics such as end-to-end query throughput, host/GPU memory footprint, and CPU/GPU utilization, as well as accuracy metrics such as context recall, query accuracy, and factual consistency. • Hyperparameters: The experiments adjust hyperparameters such as batch sizes, embedding dimensions, and indexing schemes to evaluate their impact on performance. • Ablation Studies: Analyze the impact of different components and configurations on system performance through ablation studies.

The experimental results show that RAGPerf can enable end-to-end performance breakdown for different RAG applications with minimal negative impact on application performance. • RAGPerf incurs negligible performance overhead under various configurations, validating its low-overhead performance analysis method. • RAGPerf can quantify the performance impact of various system configurations (e.g., available CPU cores, CPU memory, and GPU memory) and different RAG configurations (e.g., batch sizes, embedding dimensions, and indexing schemes). • Through testing on various datasets, RAGPerf can capture the performance behavior of real-world RAG systems, especially under diverse data update and query distribution scenarios.

RAGPerf has broad potential in various application scenarios. • Scientific Research: RAGPerf can help researchers optimize the performance of RAG systems in scientific data processing, improving data analysis accuracy and efficiency. • Legal Discovery: RAGPerf can be used for the retrieval and analysis of legal documents, helping legal practitioners quickly access relevant information. • Financial Analysis: RAGPerf can support real-time updates and analysis of financial data, improving the accuracy of financial decision-making.

Despite RAGPerf's excellent performance analysis capabilities, it may encounter performance bottlenecks when handling extremely large datasets, especially with frequent indexing and update operations. Additionally, the complexity of RAGPerf's configuration may present a learning curve for beginners. Future research directions include extending RAGPerf to support more databases and embedding models, and optimizing its performance on ultra-large datasets.