RAGServe: Fast Quality-Aware RAG Systems with Configuration Adaptation (2412.10543v1)

Abstract: RAG (Retrieval Augmented Generation) allows LLMs to generate better responses with external knowledge, but using more external knowledge often improves generation quality at the expense of response delay. Prior work either reduces the response delay (through better scheduling of RAG queries) or strives to maximize quality (which involves tuning the RAG workflow), but they fall short in optimizing the tradeoff between the delay and quality of RAG responses. This paper presents RAGServe, the first RAG system that jointly schedules queries and adapts the key RAG configurations of each query, such as the number of retrieved text chunks and synthesis methods, in order to balance quality optimization and response delay reduction. Using 4 popular RAG-QA datasets, we show that compared with the state-of-the-art RAG optimization schemes, RAGServe reduces the generation latency by $1.64-2.54\times$ without sacrificing generation quality.

• The paper introduces RAGServe, the first system to jointly schedule queries and adaptively select configurations for RAG systems to balance speed and quality.
• RAGServe employs an LLM-based profiler for per-query configuration adaptation, considering query characteristics and system resources for optimal processing.
• Empirical evaluation shows RAGServe reduces RAG generation latency by 1.64 imes to 2.54 imes and boosts throughput by 1.8 imes to 4.5 imes compared to state-of-the-art, maintaining quality.

A Review of "RAGServe: Fast Quality-Aware RAG Systems with Configuration Adaptation"

The paper "RAGServe: Fast Quality-Aware RAG Systems with Configuration Adaptation" introduces RAGServe, an innovative system designed to optimize Retrieval-Augmented Generation (RAG) processes by balancing between the generation quality and response delay. The authors provide a comprehensive framework that dynamically adjusts key RAG system configurations on a per-query basis, addressing the inherent trade-offs between response delay and output quality that are characteristic of RAG operations.

Key Contributions and Methodology

RAGServe is proposed as the first system to jointly schedule queries and adaptively select configurations for RAG systems. This is crucial for optimizing the trade-offs between generation latency and output quality. The system incorporates several novel aspects:

1. Integration of Configuration Adaptation with Scheduling: Unlike prior approaches, which either focused on optimizing query scheduling without considering individual query needs or aimed at maximizing output quality at the expense of speed, RAGServe strategically combines the two methodologies. It intelligently selects configurations such as the number of context chunks retrieved and the synthesis methods used.
2. Per-Query Configuration Adaptation: RAGServe distinguishes itself by tailoring RAG configurations to the specific requirements of each query. This approach leverages an LLM-based profiler that assesses query complexity, the necessity for joint reasoning, and other query characteristics that inform configuration decisions.
3. Joint Decision-Making with System Resource Considerations: By leveraging a narrowed configuration space generated from initial query profiling, RAGServe makes joint decisions about configuration selection and scheduling based on current system resources. This mitigates the delay that might occur from GPU memory constraints and enables more efficient processing.

Empirical Evaluation

Using four popular RAG-QA datasets, the evaluation of RAGServe demonstrates significant reduction in generation latency, ranging from 1.64× to 2.54×, compared to state-of-the-art RAG optimization schemes, without compromising on the response quality. The system also boosts throughput by 1.8× to 4.5×, underscoring its efficiency.

Practical and Theoretical Implications

Practically, RAGServe's ability to dynamically adapt configurations for each query suggests it could significantly improve the responsiveness of RAG systems used in applications that demand high-quality outputs quickly, such as real-time chatbots and personal assistants. Theoretically, this work presents a significant step forward in understanding the intricacies of balancing delay and quality, paving the way for further research seeking to address similar trade-offs in other adaptive systems.

Future Directions

While RAGServe effectively navigates the complexity of optimizing RAG systems, there are areas ripe for further exploration. Future work could explore integrating more sophisticated machine learning models for profiling and configuration prediction, potentially enhancing the adaptability and precision of RAGServe. Moreover, expanding RAGServe to work seamlessly with emerging retrieval techniques and more diverse datasets could underscore its utility and adaptability.

In conclusion, RAGServe represents a significant advancement in the development of RAG systems. By effectively balancing quality and latency through intelligent query adaptation and scheduling, it lays a foundation for refined methodologies in real-time, knowledge-intensive AI applications.