A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data (2412.05838v1)

Abstract: Retrieval-Augmented Generation (RAG) enhances LLMs by incorporating external, domain-specific data into the generative process. While LLMs are highly capable, they often rely on static, pre-trained datasets, limiting their ability to integrate dynamic or private data. Traditional RAG systems typically use a single-agent architecture to handle query generation, data retrieval, and response synthesis. However, this approach becomes inefficient when dealing with diverse data sources, such as relational databases, document stores, and graph databases, often leading to performance bottlenecks and reduced accuracy. This paper proposes a multi-agent RAG system to address these limitations. Specialized agents, each optimized for a specific data source, handle query generation for relational, NoSQL, and document-based systems. These agents collaborate within a modular framework, with query execution delegated to an environment designed for compatibility across various database types. This distributed approach enhances query efficiency, reduces token overhead, and improves response accuracy by ensuring that each agent focuses on its specialized task. The proposed system is scalable and adaptable, making it ideal for generative AI workflows that require integration with diverse, dynamic, or private data sources. By leveraging specialized agents and a modular execution environment, the system provides an efficient and robust solution for handling complex, heterogeneous data environments in generative AI applications.

• The paper proposes a collaborative multi-agent RAG framework utilizing specialized agents for different data types and a centralized polyglot query execution platform.
• The system employs a generative agent and few-shot prompt engineering to synthesize retrieved data into accurate, contextually relevant responses efficiently.
• This modular approach offers scalability and adaptability for diverse industry applications, integrating easily with private and dynamic data sources.

The paper "A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data Sources" explores the development and implementation of a sophisticated Multi-Agent RAG system designed to mitigate the inefficiencies observed in traditional single-agent RAG architectures, particularly in environments with diverse data sources. The authors propose a novel framework that leverages multiple specialized agents to improve query handling and data integration, addressing performance bottlenecks associated with heterogeneous data types.

Key Contributions and Methodology:

1. Multi-Agent Framework: The system decentralizes the roles of query generation, data retrieval, and response synthesis by employing specialized agents, each tailored for different database types. These include agents optimized for relational databases (e.g., MySQL), document-based systems (e.g., MongoDB), and graph databases (e.g., Neo4j). This specialization allows for higher precision and efficiency, ensuring that the system can scale effectively and handle complex queries across diverse data environments.
2. Centralized Query Execution: Once queries are generated, they are executed within a centralized environment supporting polyglot databases. This ensures compatibility across various data storage formats, facilitating seamless data retrieval and minimizing redundancies associated with executing queries directly from agents.
3. Generative Agent: The architecture introduces a generative agent that synthesizes the retrieved data into meaningful responses. This agent integrates both the user query and the retrieved context, ensuring accurate and contextually relevant outputs. The system is designed to reduce token overhead by streamlining the interaction between the generative processes and the specialized retrieval tasks.
4. Prompt Engineering and Adaptive Learning: The system utilizes prompt engineering, particularly few-shot prompting, to enhance query generation accuracy. This approach involves providing structured examples to the agents for optimal query formulation. Additionally, the paper highlights potential future directions in adaptive learning mechanisms, which can refine agent outputs based on user interactions and evolving data contexts.
5. Scalability and Industry Application: The proposed Multi-Agent RAG system's modular architecture is highly adaptable, making it suitable for varied applications across industries such as healthcare, finance, and logistics. Its ability to integrate with private and dynamic data sources without significant modifications underscores its robustness and practicality for real-world implementations.

Potential for Future Research:

The authors suggest several directions for advancing the multi-agent paradigm, including refining inter-agent communication, enhancing adaptive learning capabilities, and optimizing prompt engineering techniques. These challenges offer pathways to further improve the system's efficiency, scalability, and versatility in processing increasingly complex and diverse data environments.

In conclusion, the presented Multi-Agent RAG framework provides a comprehensive solution for addressing the constraints of traditional RAG systems, particularly in handling dynamic and heterogeneous data sources. By distributing the roles of query handling and data integration among specialized agents and leveraging a centralized query execution platform, the system optimizes retrieval accuracy and generative quality, paving the way for more sophisticated applications of AI in data-driven contexts.