assistant/agent/agents/customer_service.py

"""
Customer Service Agent - Handles FAQ and general inquiries
"""
import json
from typing import Any

from core.state import AgentState, ConversationState, add_tool_call, set_response
from core.llm import get_llm_client, Message
from prompts import get_prompt
from utils.logger import get_logger
from utils.faq_library import get_faq_library

logger = get_logger(__name__)


async def customer_service_agent(state: AgentState) -> AgentState:
    """Customer service agent node

    Handles FAQ, company info, and general inquiries using Strapi MCP tools.

    Args:
        state: Current agent state

    Returns:
        Updated state with tool calls or response
    """
    logger.info(
        "Customer service agent processing",
        conversation_id=state["conversation_id"]
    )

    state["current_agent"] = "customer_service"
    state["agent_history"].append("customer_service")
    state["state"] = ConversationState.PROCESSING.value

    # Check if we have tool results to process
    if state["tool_results"]:
        return await _generate_response_from_results(state)

    # ========== FAST PATH: Check if FAQ was already matched at router ==========
    # Router already checked FAQ and stored response if found
    if "faq_response" in state and state["faq_response"]:
        logger.info(
            "Using FAQ response from router",
            conversation_id=state["conversation_id"],
            response_length=len(state["faq_response"])
        )
        return set_response(state, state["faq_response"])
    # =========================================================================

    # ========== FAST PATH: Check local FAQ library first (backup) ==========
    # This provides instant response for common questions without API calls
    # This is a fallback in case FAQ wasn't matched at router level
    faq_library = get_faq_library()
    faq_response = faq_library.find_match(state["current_message"])

    if faq_response:
        logger.info(
            "FAQ match found, returning instant response",
            conversation_id=state["conversation_id"],
            response_length=len(faq_response)
        )
        return set_response(state, faq_response)
    # ============================================================

    # Get detected language
    locale = state.get("detected_language", "en")

    # Check if we have already queried FAQ
    tool_calls = state.get("tool_calls", [])
    has_faq_query = any(tc.get("tool_name") in ["query_faq", "search_knowledge_base"] for tc in tool_calls)

    # ========== ROUTING: Use sub_intent from router if available ==========
    # Router already classified the intent, use it for direct FAQ query
    sub_intent = state.get("sub_intent")

    # Map sub_intent to FAQ category
    sub_intent_to_category = {
        "register_inquiry": "register",
        "order_inquiry": "order",
        "payment_inquiry": "payment",
        "shipment_inquiry": "shipment",
        "return_inquiry": "return",
        "policy_inquiry": "return",  # Policy queries use return FAQ
    }

    # Check if we should auto-query FAQ based on sub_intent
    if sub_intent in sub_intent_to_category and not has_faq_query:
        category = sub_intent_to_category[sub_intent]
        logger.info(
            f"Auto-querying FAQ based on sub_intent: {sub_intent} -> category: {category}",
            conversation_id=state["conversation_id"]
        )

        state = add_tool_call(
            state,
            tool_name="query_faq",
            arguments={
                "category": category,
                "locale": locale,
                "limit": 5
            },
            server="strapi"
        )
        state["state"] = ConversationState.TOOL_CALLING.value
        return state
    # ========================================================================

    # Auto-detect category and query FAQ (fallback if sub_intent not available)
    message_lower = state["current_message"].lower()

    # 定义分类关键词（支持多语言：en, nl, de, es, fr, it, tr, zh）
    category_keywords = {
        "register": [
            # English
            "register", "sign up", "account", "login", "password", "forgot",
            # Dutch (Nederlands)
            "registreren", "account", "inloggen", "wachtwoord",
            # German (Deutsch)
            "registrieren", "konto", "anmelden", "passwort",
            # Spanish (Español)
            "registrar", "cuenta", "iniciar", "contraseña",
            # French (Français)
            "enregistrer", "compte", "connecter", "mot de passe",
            # Italian (Italiano)
            "registrarsi", "account", "accesso", "password",
            # Turkish (Türkçe)
            "kayıt", "hesap", "giriş", "şifre",
            # Chinese (中文)
            "注册", "账号", "登录", "密码", "忘记密码"
        ],
        "order": [
            # English
            "order", "place order", "cancel order", "modify order", "change order",
            # Dutch
            "bestelling", "bestellen", "annuleren", "wijzigen",
            # German
            "bestellung", "bestellen", "stornieren", "ändern",
            # Spanish
            "pedido", "hacer pedido", "cancelar", "modificar",
            # French
            "commande", "passer commande", "annuler", "modifier",
            # Italian
            "ordine", "ordinare", "cancellare", "modificare",
            # Turkish
            "sipariş", "sipariş ver", "iptal", "değiştir",
            # Chinese
            "订单", "下单", "取消订单", "修改订单", "更改订单"
        ],
        "payment": [
            # English
            "pay", "payment", "checkout", "voucher", "discount", "promo",
            # Dutch
            "betalen", "betaling", "korting", "voucher",
            # German
            "bezahlen", "zahlung", "rabatt", "gutschein",
            # Spanish
            "pagar", "pago", "descuento", "cupón",
            # French
            "payer", "paiement", "réduction", "bon",
            # Italian
            "pagare", "pagamento", "sconto", "voucher",
            # Turkish
            "ödemek", "ödeme", "indirim", "kupon",
            # Chinese
            "支付", "付款", "结算", "优惠券", "折扣", "促销"
        ],
        "shipment": [
            # English
            "ship", "shipping", "delivery", "courier", "transit", "logistics", "tracking",
            # Dutch
            "verzenden", "levering", "koerier", "logistiek", "volgen",
            # German
            "versand", "lieferung", "kurier", "logistik", "verfolgung",
            # Spanish
            "enviar", "envío", "entrega", "mensajería", "logística", "seguimiento",
            # French
            "expédier", "livraison", "coursier", "logistique", "suivi",
            # Italian
            "spedire", "spedizione", "consegna", "corriere", "logistica", "tracciamento",
            # Turkish
            "gönderi", "teslimat", "kurye", "lojistik", "takip",
            # Chinese
            "发货", "配送", "快递", "物流", "运输", "配送单"
        ],
        "return": [
            # English
            "return", "refund", "exchange", "defective", "damaged",
            # Dutch
            "retour", "terugbetaling", "ruilen", "defect",
            # German
            "rückgabe", "erstattung", "austausch", "defekt",
            # Spanish
            "devolución", "reembolso", "cambio", "defectuoso",
            # French
            "retour", "remboursement", "échange", "défectueux",
            # Italian
            "reso", "rimborso", "cambio", "difettoso",
            # Turkish
            "iade", "geri ödeme", "değişim", "defekt",
            # Chinese
            "退货", "退款", "换货", "有缺陷", "损坏"
        ],
    }

    # 检测分类（仅在未通过 sub_intent 匹配时使用）
    detected_category = None
    for category, keywords in category_keywords.items():
        if any(keyword in message_lower for keyword in keywords):
            detected_category = category
            break

    # 如果检测到分类且未查询过 FAQ，自动查询
    if detected_category and not has_faq_query:
        logger.info(
            f"Auto-querying FAQ for category: {detected_category}",
            conversation_id=state["conversation_id"]
        )

        # 自动添加 FAQ 工具调用
        state = add_tool_call(
            state,
            tool_name="query_faq",
            arguments={
                "category": detected_category,
                "locale": locale,
                "limit": 5
            },
            server="strapi"
        )
        state["state"] = ConversationState.TOOL_CALLING.value
        return state

    # 如果询问营业时间或联系方式，自动查询公司信息
    if any(keyword in message_lower for keyword in ["opening hour", "contact", "address", "phone", "email"]) and not has_faq_query:
        logger.info(
            "Auto-querying company info",
            conversation_id=state["conversation_id"]
        )

        state = add_tool_call(
            state,
            tool_name="get_company_info",
            arguments={
                "section": "contact",
                "locale": locale
            },
            server="strapi"
        )
        state["state"] = ConversationState.TOOL_CALLING.value
        return state
    
    # Build messages for LLM
    # Load prompt in detected language
    system_prompt = get_prompt("customer_service", locale)

    messages = [
        Message(role="system", content=system_prompt),
    ]
    
    # Add conversation history
    for msg in state["messages"][-6:]:
        messages.append(Message(role=msg["role"], content=msg["content"]))
    
    # Add current message
    messages.append(Message(role="user", content=state["current_message"]))
    
    try:
        llm = get_llm_client()
        response = await llm.chat(messages, temperature=0.7)

        # Log raw response for debugging
        logger.info(
            "Customer service LLM response",
            conversation_id=state["conversation_id"],
            response_preview=response.content[:300] if response.content else "EMPTY",
            response_length=len(response.content) if response.content else 0
        )

        # Parse response
        content = response.content.strip()

        # Handle markdown code blocks
        if content.startswith("```"):
            parts = content.split("```")
            if len(parts) >= 2:
                content = parts[1]
                if content.startswith("json"):
                    content = content[4:]
                content = content.strip()

        try:
            result = json.loads(content)
            action = result.get("action")

            if action == "call_tool":
                # Add tool call to state
                state = add_tool_call(
                    state,
                    tool_name=result["tool_name"],
                    arguments=result.get("arguments", {}),
                    server="strapi"
                )
                state["state"] = ConversationState.TOOL_CALLING.value

            elif action == "respond":
                state = set_response(state, result["response"])
                state["state"] = ConversationState.GENERATING.value

            elif action == "handoff":
                state["requires_human"] = True
                state["handoff_reason"] = result.get("reason", "User request")
            else:
                # Unknown action, treat as plain text response
                logger.warning(
                    "Unknown action in LLM response",
                    action=action,
                    conversation_id=state["conversation_id"]
                )
                state = set_response(state, response.content)

            return state

        except json.JSONDecodeError as e:
            # JSON parsing failed - try alternative format: "tool_name\n{args}"
            logger.error(
                "Failed to parse LLM response as JSON",
                error=str(e),
                raw_content=content[:500],
                conversation_id=state["conversation_id"]
            )

            # Handle non-JSON format: "tool_name\n{args}"
            if '\n' in content and not content.startswith('{'):
                lines = content.split('\n', 1)
                tool_name = lines[0].strip()
                args_json = lines[1].strip() if len(lines) > 1 else '{}'

                try:
                    arguments = json.loads(args_json) if args_json else {}
                    logger.info(
                        "Customer service agent calling tool (alternative format)",
                        tool_name=tool_name,
                        arguments=arguments,
                        conversation_id=state["conversation_id"]
                    )

                    state = add_tool_call(
                        state,
                        tool_name=tool_name,
                        arguments=arguments,
                        server="strapi"
                    )
                    state["state"] = ConversationState.TOOL_CALLING.value
                    return state
                except json.JSONDecodeError:
                    # Args parsing also failed
                    logger.warning(
                        "Failed to parse tool arguments",
                        tool_name=tool_name,
                        args_json=args_json[:200],
                        conversation_id=state["conversation_id"]
                    )
                    state = set_response(state, "抱歉，我无法理解您的请求。请尝试重新表述或联系人工客服。")
                    return state
            else:
                # Not a recognized format
                state = set_response(state, "抱歉，我无法理解您的请求。请尝试重新表述或联系人工客服。")
                return state

    except Exception as e:
        logger.error("Customer service agent failed", error=str(e), exc_info=True)
        state["error"] = str(e)
        return state


async def _generate_response_from_results(state: AgentState) -> AgentState:
    """Generate response based on tool results"""

    # Build context from tool results - extract only essential info to reduce prompt size
    tool_context = []
    for result in state["tool_results"]:
        if result["success"]:
            tool_name = result['tool_name']
            data = result['data']

            # Extract only essential information based on tool type
            if tool_name == "get_company_info":
                # Extract key contact info only
                contact = data.get('contact', {})
                emails = contact.get('email', [])
                if isinstance(emails, list) and emails:
                    email_str = ", ".join(emails[:3])  # Max 3 emails
                else:
                    email_str = str(emails) if emails else "N/A"

                phones = contact.get('phone', [])
                if isinstance(phones, list) and phones:
                    phone_str = ", ".join(phones[:2])  # Max 2 phones
                else:
                    phone_str = str(phones) if phones else "N/A"

                address = contact.get('address', {})
                address_str = f"{address.get('city', '')}, {address.get('country', '')}".strip(', ')

                summary = f"Contact Information: Emails: {email_str} | Phones: {phone_str} | Address: {address_str} | Working hours: {contact.get('working_hours', 'N/A')}"
                tool_context.append(summary)

            elif tool_name == "query_faq" or tool_name == "search_knowledge_base":
                # Extract FAQ items summary
                faqs = data.get('faqs', []) if isinstance(data, dict) else []
                if faqs:
                    faq_summaries = [f"- Q: {faq.get('question', '')[:50]}... A: {faq.get('answer', '')[:50]}..." for faq in faqs[:3]]
                    summary = f"Found {len(faqs)} FAQ items:\n" + "\n".join(faq_summaries)
                    tool_context.append(summary)
                else:
                    tool_context.append("No FAQ items found")

            elif tool_name == "get_categories":
                # Extract category names only
                categories = data.get('categories', []) if isinstance(data, dict) else []
                category_names = [cat.get('name', '') for cat in categories[:5] if cat.get('name')]
                summary = f"Available categories: {', '.join(category_names)}"
                if len(categories) > 5:
                    summary += f" (and {len(categories) - 5} more)"
                tool_context.append(summary)

            else:
                # For other tools, include concise summary (limit to 200 chars)
                data_str = json.dumps(data, ensure_ascii=False)[:200]
                tool_context.append(f"Tool {tool_name} returned: {data_str}...")

        else:
            tool_context.append(f"Tool {result['tool_name']} failed: {result['error']}")

    prompt = f"""Based on the following tool returned information, generate a response to the user.

User question: {state["current_message"]}

Tool returned information:
{chr(10).join(tool_context)}

Please generate a friendly and professional response in Chinese. Keep it concise but informative.
If the tool did not return useful information, honestly inform the user and suggest other ways to get help.
Return only the response content, do not return JSON."""

    messages = [
        Message(role="system", content="You are a professional B2B customer service assistant, please answer user questions based on tool returned information."),
        Message(role="user", content=prompt)
    ]

    try:
        llm = get_llm_client()
        # Lower temperature for faster response
        response = await llm.chat(messages, temperature=0.3)
        state = set_response(state, response.content)
        return state

    except Exception as e:
        logger.error("Response generation failed", error=str(e))
        state = set_response(state, "抱歉，处理您的请求时出现问题。请稍后重试或联系人工客服。")
        return state