src/nodes/intent_classifier.py

from pydantic import BaseModel
from typing import Literal, List
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from src.state import AgentState
from src.utils.llm_factory import get_llm, _invoke_with_backoff
from src.utils.prompt_templates import INTENT_SYSTEM, INTENT_FEW_SHOTS
import json

# Emotion keywords that suggest hybrid intent
EMOTION_KEYWORDS = [
    "frustrated", "angry", "upset", "disappointed", "furious", "livid",
    "annoyed", "irritated", "unhappy", "dissatisfied", "terrible",
    "awful", "horrible", "unacceptable", "ridiculous", "outrageous"
]

class IntentClassification(BaseModel):
    intent: Literal["transactional", "informational", "sentimental", "hybrid", "out_of_scope"]
    confidence: float
    sub_intents: List[str]
    reasoning: str

def classify_intent(state: AgentState) -> dict:
    """Classify user intent with fallback to hybrid on errors"""
    user_input = state.get("user_input", "").lower()
    
    # Check for emotion keywords - if found with order context, force hybrid
    has_emotion = any(keyword in user_input for keyword in EMOTION_KEYWORDS)
    has_order_context = state.get("last_order_id") is not None
    
    if has_emotion and has_order_context:
        print(f"[intent_classifier] Emotion keyword detected with order context, forcing hybrid intent")
        return {
            "intent": "hybrid",
            "intent_confidence": 0.85,
            "sub_intents": ["order_tracking", "complaint"]
        }
    
    try:
        llm = get_llm(temperature=0.0)

        messages = [SystemMessage(content=INTENT_SYSTEM)]
        
        # Inject few-shots
        for shot in INTENT_FEW_SHOTS:
            if shot["role"] == "user":
                messages.append(HumanMessage(content=shot["content"]))
            else:
                messages.append(SystemMessage(content=shot["content"]))
        
        # Inject recent conversation history for pronoun resolution
        recent_msgs = state.get("messages", [])[-4:]  # Last 2 turns
        if recent_msgs:
            history_context = "\n[Recent conversation context for pronoun resolution:]\n"
            for msg in recent_msgs:
                if isinstance(msg, HumanMessage):
                    history_context += f"User: {msg.content[:150]}\n"
                else:
                    history_context += f"Agent: {msg.content[:150]}\n"
            messages.append(SystemMessage(content=history_context))
        
        messages.append(HumanMessage(content=state.get("user_input", "")))

        response = _invoke_with_backoff(llm, messages, provider="groq")
        raw = response.content.strip()
        
        # Strip markdown fences if LLM wraps in ```json
        raw = raw.replace("```json", "").replace("```", "").strip()
        parsed = IntentClassification(**json.loads(raw))

        # Handle out_of_scope as terminal path
        if parsed.intent == "out_of_scope":
            print(f"[intent_classifier] Out-of-scope query detected (confidence: {parsed.confidence:.2f})")
            
            # Check if it's a product browsing query for custom message
            user_input_lower = state.get("user_input", "").lower()
            is_product_search = any(keyword in user_input_lower for keyword in [
                "product", "catalog", "browse", "list", "show items", "search products"
            ])
            
            if is_product_search:
                final_response = (
                    "I'm a customer support assistant focused on helping with existing orders, "
                    "deliveries, returns, and refunds. For browsing products or searching our "
                    "catalog, please visit the Olist marketplace directly.\n\n"
                    "Is there anything I can help you with regarding an existing order?"
                )
            else:
                final_response = (
                    "I'm Olist's customer support assistant — I can help with orders, "
                    "deliveries, returns, payments, and seller queries. "
                    "Your question doesn't seem related to Olist support. "
                    "Could you ask me something about your Olist experience?"
                )
            
            return {
                "intent": "out_of_scope",
                "intent_confidence": parsed.confidence,
                "sub_intents": parsed.sub_intents,
                "final_response": final_response,
                "messages": [
                    HumanMessage(content=state.get("user_input", "")),
                    AIMessage(content=final_response)
                ]
            }

        # Robustness: low confidence → force hybrid
        if parsed.confidence < 0.6:
            return {
                "intent": "hybrid",
                "intent_confidence": parsed.confidence,
                "sub_intents": parsed.sub_intents,
                "error_log": state.get("error_log", []) + [
                    f"[intent_classifier] Low confidence ({parsed.confidence:.2f}), forced hybrid"
                ]
            }
        
        return {
            "intent": parsed.intent,
            "intent_confidence": parsed.confidence,
            "sub_intents": parsed.sub_intents
        }
        
    except Exception as e:
        # Rule-based fallback to avoid complete failure
        user_input = state.get("user_input", "").lower()
        
        # Simple keyword matching
        if any(word in user_input for word in ["order", "package", "delivery", "tracking", "where", "status"]):
            intent = "transactional"
            confidence = 0.6
            sub_intents = ["order_tracking"]
        elif any(word in user_input for word in ["policy", "return", "refund", "warranty", "payment", "shipping"]):
            intent = "informational"
            confidence = 0.6
            sub_intents = ["policy_query"]
        elif any(word in user_input for word in ["angry", "frustrated", "upset", "terrible", "awful"]):
            intent = "sentimental"
            confidence = 0.6
            sub_intents = ["complaint"]
        else:
            intent = "hybrid"
            confidence = 0.5
            sub_intents = ["default"]
        
        return {
            "intent": intent,
            "intent_confidence": confidence,
            "sub_intents": sub_intents,
            "error_log": state.get("error_log", []) + [
                f"[intent_classifier] LLM error, using rule-based fallback: {str(e)[:100]}"
            ]
        }