[Improvement] Multi-Agent Architecture Enhancement

[iziang]

Is your improvement request related to a problem? Please describe.

Currently, ApeRAG uses a single-agent architecture where one monolithic agent handles the entire RAG pipeline from query understanding, retrieval strategy planning, content retrieval, result evaluation, to final response generation. This "jack-of-all-trades" approach leads to attention dispersion and suboptimal performance in each specialized task. While this approach works, it has several limitations:

1. Attention dispersion: The single agent must handle multiple complex tasks simultaneously, leading to suboptimal performance in each area
2. Monolithic prompt management: All prompts are managed in a single place, making it difficult to optimize prompts for specific tasks
3. Limited specialization: The agent cannot deeply focus on and optimize for specific tasks like retrieval strategy, content evaluation, or response generation
4. Scalability concerns: All processing happens in a single execution context, limiting parallel processing and resource optimization
5. Maintenance complexity: As the system grows, managing a single large agent with multiple responsibilities becomes increasingly difficult

If this is a new function, please describe the motivation and goals.

The goal is to transform ApeRAG from a single-agent system into a collaborative multi-agent architecture where specialized agents work together to generate high-quality responses. This approach will:

Motivation:

• Improved modularity: Each agent focuses on a specific task, making the system easier to understand, test, and maintain
• Better prompt optimization: Each agent can have specialized prompts tailored to its specific function
• Enhanced decision-making: Agents can make intelligent decisions based on their expertise and context
• Increased parallelization: Multiple agents can work simultaneously on different aspects of the query
• Better quality control: Specialized evaluation agents can assess and improve response quality
• Easier extensibility: New agents can be added without affecting existing ones

Goals:

1. Decompose the current monolithic agent into specialized agents
2. Implement inter-agent communication and coordination mechanisms
3. Enable dynamic workflow orchestration based on query characteristics
4. Improve overall response quality through specialized expertise
5. Maintain backward compatibility with existing APIs

Describe the solution you'd like

Proposed Multi-Agent Architecture:

1. Task Planning Agent

   • Analyzes incoming queries and determines the optimal retrieval strategy
   • Decides which retrieval agents to invoke and in what order
   • Manages the overall workflow and coordinates between agents

2. Vector Retrieval Agent

   • Specializes in semantic similarity search using vector embeddings
   • Optimized prompts for understanding semantic intent
   • Handles embedding generation and vector database queries

3. Fulltext Retrieval Agent

   • Focuses on keyword-based and exact match retrieval
   • Specialized in query expansion and term matching
   • Handles traditional search index operations

4. Graph Retrieval Agent

   • Specializes in relationship-based and knowledge graph queries
   • Understands entity relationships and graph traversal
   • Optimized for complex reasoning over connected data

5. Summary Retrieval Agent

   • Focuses on document summarization and high-level content retrieval
   • Specialized in identifying key themes and concepts
   • Handles hierarchical content organization

6. Fusion Agent

   • Combines results from multiple retrieval agents
   • Handles deduplication, ranking, and result merging
   • Optimized for understanding relevance and importance

7. Evaluation Agent

   • Assesses the quality and relevance of retrieved content
   • Provides feedback to improve retrieval strategies
   • Handles confidence scoring and result validation

8. Response Generation Agent

   • Specializes in generating final responses using retrieved context
   • Optimized prompts for answer synthesis and formatting
   • Handles citation generation and response structuring

Implementation Details:

• Agent Communication: Implement a message-passing system for inter-agent communication
• Workflow Orchestration: Create a new multi-agent coordination system for dynamic agent collaboration
• Prompt Management: Create specialized prompt templates for each agent type
• State Management: Implement shared context and state management across agents
• Monitoring: Add comprehensive logging and monitoring for multi-agent interactions

Describe alternatives you've considered

1. Enhanced Single Agent: Improving the current single-agent system with better prompt engineering and more sophisticated flow control. However, this doesn't address the fundamental scalability and maintainability issues.

2. Microservices Architecture: Breaking the system into independent microservices. While this provides good separation, it lacks the intelligent coordination and decision-making capabilities of a true multi-agent system.

3. Plugin-based Architecture: Creating a plugin system where different retrieval methods are plugins. This provides modularity but doesn't enable the intelligent coordination and specialization benefits of agents.

4. Hierarchical Agent System: A tree-like structure with a master agent and sub-agents. This is simpler but less flexible than the proposed collaborative approach.

Additional context

Current Architecture Analysis:

• The current single agent handles all tasks from query analysis to response generation
• The monolithic design leads to attention dispersion and suboptimal task-specific performance
• Existing prompt templates can be specialized and optimized for each agent type
• The agent system needs to be decomposed into multiple specialized, focused agents

Benefits:

• Better Response Quality: Specialized agents can optimize their specific tasks
• Improved Maintainability: Smaller, focused agents are easier to debug and improve
• Enhanced Scalability: Agents can be scaled independently based on demand
• Flexible Workflows: Dynamic agent coordination based on query characteristics
• Better Testing: Each agent can be tested independently

Implementation Phases:

1. Phase 1: Create specialized agent classes with dedicated prompts for each task
2. Phase 2: Implement agent communication and coordination mechanisms
3. Phase 3: Add dynamic workflow orchestration and decision-making
4. Phase 4: Implement evaluation and feedback mechanisms
5. Phase 5: Performance optimization and monitoring

Compatibility:

• Maintain existing API endpoints
• Ensure backward compatibility with existing system behavior
• Provide migration path for existing configurations

This enhancement will significantly improve ApeRAG's capabilities while maintaining its ease of use and reliability.

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