catonline_ai/vw-agentic-rag/docs/topics/USER_MANUAL_AGENT_IMPLEMENTATION.md

User Manual Agent Implementation Summary

Overview

Successfully refactored service/graph/user_manual_rag.py from a simple RAG node to a full autonomous agent, following the pattern from the main agent in service/graph/graph.py.

Key Changes

1. New Agent Node Function: user_manual_agent_node

• Implements the "detect-first-then-stream" strategy for optimal multi-round behavior
• Supports autonomous tool calling with user manual tools
• Handles streaming responses with HTML comment filtering
• Manages tool rounds and conversation trimming
• Uses user manual specific system prompt from configuration

2. User Manual Tools Integration

• Uses service/graph/user_manual_tools.py for tool schemas and tools mapping
• Specifically designed for user manual retrieval operations
• Integrated with retrieve_system_usermanual tool

3. Routing Logic: user_manual_should_continue

• Routes to user_manual_tools when tool calls are detected
• Routes to post_process when no tool calls (final synthesis completed)
• Routes to user_manual_agent for next round after tool execution

4. Tool Execution: run_user_manual_tools_with_streaming

• Executes user manual tools with streaming support
• Supports parallel execution (though typically only one tool for user manual)
• Enhanced error handling with proper error categories
• Streaming events for tool start, result, and error states

5. System Prompt Integration

• Uses user_manual_prompt from llm_prompt.yaml configuration
• Formats prompt with conversation history, context content, and current query
• Maintains grounding requirements and response structure from original prompt

Technical Implementation Details

Agent Node Features

• Tool Round Management: Tracks and limits tool calling rounds
• Conversation Trimming: Manages context length automatically
• Streaming Support: Real-time token streaming with HTML comment filtering
• Error Handling: Comprehensive error handling with user-friendly messages
• Tool Detection: Non-streaming detection followed by streaming synthesis

Routing Strategy

def user_manual_should_continue(state: AgentState) -> Literal["user_manual_tools", "user_manual_agent", "post_process"]:
    # Routes based on message type and tool calls presence

Tool Execution Strategy

• Parallel execution support (for future expansion)
• Streaming events for real-time feedback
• Error recovery with graceful fallbacks
• Tool result aggregation and state management

Configuration Integration

User Manual Prompt Template

The agent uses the existing user_manual_prompt from configuration with placeholders:

• {conversation_history}: Recent conversation context
• {context_content}: Retrieved user manual content from tools
• {current_query}: Current user question

Tool Configuration

• Tool schemas automatically generated from user manual tools
• Force tool choice enabled for autonomous operation
• Tools disabled during final synthesis to prevent hallucination

Backward Compatibility

Legacy Function Maintained

async def user_manual_rag_node(state: AgentState, config: Optional[RunnableConfig] = None) -> Dict[str, Any]:
    """Legacy user manual RAG node - redirects to new agent-based implementation"""
    return await user_manual_agent_node(state, config)

Testing Results

Functionality Tests

✅ Basic Agent Operation: Tool detection and calling works correctly
✅ Tool Execution: User manual retrieval executes successfully
✅ Routing Logic: Proper routing between agent, tools, and post-process
✅ Multi-Round Workflow: Complete workflow with tool rounds and final synthesis
✅ Streaming Support: Real-time response streaming with proper formatting

Integration Tests

✅ Configuration Loading: User manual prompt loaded correctly
✅ Tool Integration: User manual tools properly integrated
✅ Error Handling: Graceful error handling and recovery
✅ State Management: Proper state updates and tracking

Usage Example

# Create state for user manual query
state = {
    "messages": [HumanMessage(content="How do I reset my password?")],
    "session_id": "session_1",
    "intent": "User_Manual_RAG",
    "tool_rounds": 0,
    "max_tool_rounds": 3
}

# Execute user manual agent
result = await user_manual_agent_node(state)

# Handle routing
routing = user_manual_should_continue(state)
if routing == "user_manual_tools":
    tool_result = await run_user_manual_tools_with_streaming(state)

Benefits of New Implementation

1. Autonomous Operation: Can make multiple tool calls and synthesize final answers
2. Better Tool Integration: Seamless integration with user manual specific tools
3. Streaming Support: Real-time response generation for better UX
4. Error Resilience: Comprehensive error handling and recovery
5. Scalability: Easy to extend with additional user manual tools
6. Consistency: Follows same patterns as main agent for maintainability

Files Modified

• service/graph/user_manual_rag.py - Complete rewrite as agent node
• scripts/test_user_manual_agent.py - New comprehensive test suite
• scripts/test_user_manual_tool.py - Fixed import path

Next Steps

1. Integration Testing: Test with main graph workflow
2. Performance Optimization: Monitor and optimize tool execution performance
3. Enhanced Features: Consider adding more user manual specific tools
4. Documentation Update: Update main documentation with new agent capabilities

The user manual functionality has been successfully upgraded from a simple RAG implementation to a full autonomous agent while maintaining backward compatibility and following established patterns from the main agent implementation.