Multi-Agent RAG System

Overview

• Retrieval-Augmented Generation (RAG): Enhances LLMs by integrating external, domain-specific data.
• Limitation of Traditional RAG: Single-agent systems struggle with diverse data sources.
• Multi-Agent RAG Proposal: Specialized agents for different data systems (relational, NoSQL, document-based) improve efficiency and scalability.

Key Components

• Specialized Agents: Tailored for specific data types, enhancing query precision.
• Centralized Execution: Combines agent outputs efficiently.
• Generative Agent: Synthesizes coherent responses from retrieved data.

Benefits

• Scalability and Adaptability: Suitable for various industries with complex data needs.
• Efficiency: Reduces token overhead and processing latency.
• Integration: Supports diverse, dynamic, private data sources.

Technical Components

1. Query Generation Agents

• Role: Generate queries tailored to specific databases.
• Example Agents:
  • MySQL Agent: Produces SQL queries.
  • MongoDB Agent: Handles document-based queries.
  • Neo4j Agent: Manages graph-based queries.

2. Query Execution Environment

• Function: Executes queries using appropriate database drivers.
• Types of Databases Supported: Relational, NoSQL, Graph.

3. Generative Agent

• Function: Produces final, user-facing responses from retrieved data.

Methodology

Phases

1. Query Generation

   • Identify data source.
   • Use appropriate query generation agent.

2. Query Execution

   • Execute generated queries via a centralized environment.

3. Response Generation

   • Synthesize data into structured user responses.

Future Scope

• Agent Communication: Enhance inter-agent interaction.
• Adaptive Learning: Implement feedback loops for dynamic improvement.
• Prompt Engineering: Refine strategies for efficiency.

Conclusion

• Advancements: Address limitations of single-agent RAG systems.
• Applications: Suitable for healthcare, finance, logistics, etc.
• Future Directions: Focus on improving inter-agent collaboration and adaptive capabilities.

References

• Extensive studies and related work on RAG systems, LLMs, and multi-agent systems.