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
```python
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
```python
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

```python
# 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.