"""
LangChain 1.0 对话链（Normal Mode）
"""
from typing import List, Dict, Any, Optional
from langchain_core.runnables import RunnablePassthrough
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
from langchain_core.output_parsers import StrOutputParser

from ..llm.siliconflow import get_llm_client


class ConversationChain:
    """标准对话链（LangChain 1.0）"""
    
    def __init__(
        self,
        system_prompt: Optional[str] = None,
        model: Optional[str] = None
    ):
        """初始化对话链
        
        Args:
            system_prompt: 系统提示词
            model: 可选的模型名称，如 deepseek-ai/DeepSeek-V3, Qwen/QwQ-32B
        """
        _sf_client = get_llm_client(model=model)
        self.llm = _sf_client.llm
        self.client = _sf_client
        self.system_prompt = system_prompt or "你是一个专业的国土空间规划知识问答助手。请基于你的知识回答用户的问题。"
        
        # 创建带历史的Prompt模板
        self.prompt = ChatPromptTemplate.from_messages([
            SystemMessage(content=self.system_prompt),
            MessagesPlaceholder(variable_name="chat_history"),
            ("human", "{question}")
        ])
        
        # 创建对话链
        self.chain = self._create_chain()
    
    def _create_chain(self):
        """创建对话链"""
        return (
            self.prompt
            | self.llm
            | StrOutputParser()
        )
    
    def invoke(self, question: str, chat_history: List[Dict] = None) -> Dict[str, Any]:
        """同步调用"""
        history_messages = self._format_history(chat_history or [])
        answer = self.chain.invoke({
            "question": question,
            "chat_history": history_messages
        })
        
        return {
            "answer": answer,
            "sources": [],
            "metadata": {
                "mode": "normal",
                "has_history": bool(chat_history)
            }
        }
    
    async def ainvoke(self, question: str, chat_history: List[Dict] = None) -> Dict[str, Any]:
        """异步调用"""
        history_messages = self._format_history(chat_history or [])
        answer = await self.chain.ainvoke({
            "question": question,
            "chat_history": history_messages
        })
        
        return {
            "answer": answer,
            "sources": [],
            "metadata": {
                "mode": "normal",
                "has_history": bool(chat_history)
            }
        }
    
    async def astream_with_thinking(self, question: str, chat_history: List[Dict] = None):
        """流式调用（包含思考过程，捕获推理模型的真实推理内容）"""
        import time

        start_time = time.time()
        history_messages = self._format_history(chat_history or [])

        # 直接使用原始 OpenAI SDK 捕获推理内容
        prompt_value = await self.prompt.ainvoke({
            "question": question,
            "chat_history": history_messages
        })
        prompt_messages = prompt_value.to_messages()

        content_started = False
        async for event_type, content in self.client.stream_with_reasoning(prompt_messages):
            if event_type == "reasoning":
                yield {"type": "thinking", "stage": "reasoning", "message": content}
            elif event_type == "content":
                if not content_started:
                    content_started = True
                yield {"type": "chunk", "content": content}
        
        # 完成
        total_time = time.time() - start_time
        yield {
            "type": "complete",
            "metadata": {
                "total_time": round(total_time, 2)
            }
        }
    
    def _format_history(self, chat_history: List[Dict]) -> List:
        """格式化聊天历史为LangChain消息格式"""
        messages = []
        for msg in chat_history:
            if msg["role"] == "user":
                messages.append(HumanMessage(content=msg["content"]))
            elif msg["role"] == "assistant":
                messages.append(AIMessage(content=msg["content"]))
        return messages


def create_conversation_chain(system_prompt: Optional[str] = None, model: Optional[str] = None) -> ConversationChain:
    """创建对话链实例
    
    Args:
        system_prompt: 系统提示词
        model: 可选的模型名称，如 deepseek-ai/DeepSeek-V3, Qwen/QwQ-32B
    """
    return ConversationChain(system_prompt=system_prompt, model=model)