ComputeAgent/nodes/ReAct/tool_rejection_exit_node.py

from typing import Dict, Any
from langchain_core.messages import HumanMessage
from ComputeAgent.models.model_manager import ModelManager
from constant import Constants
import logging

logger = logging.getLogger("ReAct Tool Rejection Exit")

# Initialize model manager for LLM loading
model_manager = ModelManager()


async def tool_rejection_exit_node(state: Dict[str, Any]) -> Dict[str, Any]:
    """
    Node that handles the case when human declines all tool executions.
    Provides a helpful, personalized response using memory context and LLM.
    
    Args:
        state: Current ReAct state with memory fields
        
    Returns:
        Updated state with helpful exit message and final response
    """
    logger.info("🚪 User declined all tool executions - generating helpful response")
    
    # Get original query and user context
    query = state.get("query", "")
    user_id = state.get("user_id", "")
    session_id = state.get("session_id", "")
    
    # Build memory context for personalized response
    memory_context = ""
    if user_id and session_id:
        try:
            from helpers.memory import get_memory_manager
            memory_manager = get_memory_manager()
            memory_context = await memory_manager.build_context_for_node(user_id, session_id, "general")
            if memory_context:
                logger.info(f"🧠 Using memory context for tool rejection response")
        except Exception as e:
            logger.warning(f"⚠️ Could not load memory context for tool rejection: {e}")
    
    # Generate a more helpful and personalized response using LLM
    try:
        # Load LLM for generating helpful response
        llm = await model_manager.load_llm_model(Constants.DEFAULT_LLM_NAME)
        
        # Get information about what tools were proposed
        pending_tool_calls = state.get("pending_tool_calls", [])
        tool_names = [tool.get('name', 'unknown tool') for tool in pending_tool_calls] if pending_tool_calls else ["tools"]
        
        # Create prompt for helpful response
        system_prompt = Constants.GENERAL_SYSTEM_PROMPT + r"""
            You are ComputeAgent, a helpful AI assistant. The user has chosen **not to use** the recommended {', '.join(tool_names)} for their query.

            Your task is to respond in a **positive, supportive, and helpful way** that:
            1. Respectfully acknowledges their choice.
            2. Suggests alternative ways to assist them.
            3. Offers ideas on how they might **rephrase or clarify** their query for better results.
            4. Personalizes the response using any available conversation context.

            User's Query: {query}

            {f"Conversation Context: {memory_context}" if memory_context else ""}

            Provide a **helpful, encouraging, and concise response** (2-3 sentences) that guides the user toward next steps without pressuring them to use the tool.
            """

        response = await llm.ainvoke([HumanMessage(content=system_prompt)])
        exit_message = response.content.strip()
        
        logger.info(f"🤖 Generated personalized tool rejection response for user {user_id}, session {session_id}")
        
    except Exception as e:
        logger.warning(f"⚠️ Could not generate LLM response for tool rejection: {e}")
        # Fallback to enhanced static message
        if memory_context:
            exit_message = f"I understand you'd prefer not to use the suggested tools. Based on our conversation, I can try to help you in other ways. Could you please rephrase your question or let me know what specific information you're looking for? I'm here to assist you however I can."
        else:
            exit_message = "I understand you'd prefer not to use the suggested tools. I'm happy to help you in other ways! Could you please rephrase your question or provide more details about what you're looking for? I'm here to assist you with direct answers whenever possible."
    
    # Update state with final response
    updated_state = state.copy()
    updated_state["response"] = exit_message
    updated_state["current_step"] = "tool_rejection_exit"

    # Remove tools from state to avoid serialization issues
    if "tools" in updated_state:
        del updated_state["tools"]

    logger.info("✅ Tool rejection exit complete with helpful response")

    return updated_state