"""
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 utils.logger import get_logger

logger = get_logger(__name__)


CUSTOMER_SERVICE_PROMPT = """你是一个专业的 B2B 购物网站客服助手。
你的职责是回答用户的一般性问题，包括：
- 常见问题解答 (FAQ)
- 公司信息查询
- 政策咨询（退换货政策、隐私政策等）
- 产品使用指南
- 其他一般性咨询

## 可用工具

你可以使用以下工具获取信息：
1. **query_faq** - 搜索 FAQ 常见问题
   - query: 搜索关键词
   - category: 分类（可选）
   
2. **get_company_info** - 获取公司信息
   - section: 信息类别（about_us, contact, etc.）
   
3. **get_policy** - 获取政策文档
   - policy_type: 政策类型（return_policy, privacy_policy, etc.）

## 工具调用格式

当需要使用工具时，请返回 JSON 格式：
```json
{
  "action": "call_tool",
  "tool_name": "工具名称",
  "arguments": {
    "参数名": "参数值"
  }
}
```

当可以直接回答时，请返回：
```json
{
  "action": "respond",
  "response": "回复内容"
}
```

当需要转人工时，请返回：
```json
{
  "action": "handoff",
  "reason": "转人工原因"
}
```

## 注意事项
- 保持专业、友好的语气
- 如果不确定答案，建议用户联系人工客服
- 不要编造信息，只使用工具返回的数据
"""


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)
    
    # Build messages for LLM
    messages = [
        Message(role="system", content=CUSTOMER_SERVICE_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)
        
        # Parse response
        content = response.content.strip()
        if content.startswith("```"):
            content = content.split("```")[1]
            if content.startswith("json"):
                content = content[4:]
        
        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")
            
        return state
        
    except json.JSONDecodeError:
        # LLM returned plain text, use as response
        state = set_response(state, response.content)
        return state
        
    except Exception as e:
        logger.error("Customer service agent failed", error=str(e))
        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
    tool_context = []
    for result in state["tool_results"]:
        if result["success"]:
            tool_context.append(f"工具 {result['tool_name']} 返回:\n{json.dumps(result['data'], ensure_ascii=False, indent=2)}")
        else:
            tool_context.append(f"工具 {result['tool_name']} 执行失败: {result['error']}")
    
    prompt = f"""基于以下工具返回的信息，生成对用户的回复。

用户问题: {state["current_message"]}

工具返回信息:
{chr(10).join(tool_context)}

请生成一个友好、专业的回复。如果工具没有返回有用信息，请诚实告知用户并建议其他方式获取帮助。
只返回回复内容，不要返回 JSON。"""
    
    messages = [
        Message(role="system", content="你是一个专业的 B2B 客服助手，请根据工具返回的信息回答用户问题。"),
        Message(role="user", content=prompt)
    ]
    
    try:
        llm = get_llm_client()
        response = await llm.chat(messages, temperature=0.7)
        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，处理您的请求时遇到问题。请稍后重试或联系人工客服。")
        return state