from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain
from langchain_huggingface.embeddings.huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_core.documents import Document
import os
from prompt_template import get_prompt
from fastapi.responses import StreamingResponse
import json
import asyncio
import numpy as np
from config import get_model_config
import pandas as pd
from typing import List, Dict, Any
import traceback

class SQLGenerator:
    def __init__(self, model_type: str = "openai"):
        # 获取模型配置
        model_config = get_model_config(model_type)

        # 初始化LLM
        self.llm = ChatOpenAI(
            model_name=model_config.model_name,
            temperature=model_config.temperature,
            api_key=model_config.api_key,
            base_url=model_config.api_base,
            max_tokens=model_config.max_tokens,
            streaming=True
        )

        # 初始化提示词模板
        self.prompt = ChatPromptTemplate.from_template(get_prompt())
        
        # 构建链式调用
        self.chain = (
            {
                "chat_history": lambda x: self._format_chat_history(x["similar_examples"]),
                "question": lambda x: x["question"]
            }
            | self.prompt
            | self.llm
            | StrOutputParser()
        )

        # 初始化本地m3e-base模型
        self.model_path = r"E:\项目临时\AI大模型\m3e-base"
        self.embeddings = HuggingFaceEmbeddings(
            model_name=self.model_path,
            model_kwargs={'device': 'cpu'},
            encode_kwargs={'normalize_embeddings': True}
        )

        # 加载示例数据并创建向量数据库
        self.examples = self._load_examples()
        self.vectorstore = self._build_vectorstore()

    def _load_examples(self):
        """加载示例数据"""
        with open('examples.json', 'r', encoding='utf-8') as f:
            return json.load(f)

    def _build_vectorstore(self):
        """构建FAISS向量数据库"""
        # 准备Document对象列表
        documents = []
        for example in self.examples:
            doc = Document(
                page_content=example['query'],
                metadata={
                    'query_type': example['query_type'],
                    'plan': example['plan'],
                    'sql_code': example['sql_code']
                }
            )
            documents.append(doc)

        # 创建FAISS向量数据库
        vectorstore = FAISS.from_documents(
            documents=documents,
            embedding=self.embeddings
        )

        return vectorstore

    def _get_similar_examples(self, query: str, k: int = 3):
        """获取最相似的示例"""
        # 使用FAISS搜索相似示例
        docs = self.vectorstore.similarity_search_with_score(query, k=k)

        # 格式化返回结果
        similar_examples = []
        for doc, score in docs:
            similar_examples.append({
                'query_type': doc.metadata['query_type'],
                'query': doc.page_content,
                'plan': doc.metadata['plan'],
                'sql_code': doc.metadata['sql_code'],
                'similarity_score': float(score)
            })

        return similar_examples

    def _format_chat_history(self, similar_examples: List[Dict[str, Any]]) -> str:
        """格式化聊天历史"""

        if len(similar_examples) == 0:
            return ""

        chat_history = "基于以下相似示例：\n\n"
        for i, example in enumerate(similar_examples, 1):
            chat_history += f"示例 {i}:\n"
            chat_history += f"问题: {example['query']}\n"
            chat_history += f"计划: {example['plan']}\n"
            chat_history += f"SQL: {example['sql_code']}\n"
            chat_history += f"相似度: {example['similarity_score']:.2f}\n\n"

        print(chat_history+"  !!!!!")
        return chat_history

    async def generate_sql_stream(self, query_description: str):
        """
        流式生成SQL查询语句
        """
        try:
            # 开始生成SQL
            yield json.dumps({"type": "start", "message": "开始生成SQL查询..."}) + "\n"

            # 获取相似示例
            similar_examples = self._get_similar_examples(query_description)
            yield json.dumps({
                "type": "similar_examples",
                "content": similar_examples
            }, ensure_ascii=False) + "\n"

            # 准备输入数据
            chain_input = {
                "similar_examples": similar_examples,
                "question": query_description
            }

            print("Chain input:", chain_input)

            try:
                # 流式生成SQL
                async for chunk in self.chain.astream(chain_input):
                    if chunk:
                        yield json.dumps({
                            "type": "sql_generation",
                            "content": chunk
                        }) + "\n"

                yield json.dumps({"type": "end", "message": "SQL生成完成"}, ensure_ascii=False) + "\n"
            except Exception as e:
                print(f"Error during streaming: {str(e)}")
                print(f"Error type: {type(e)}")
                print(f"Error details: {traceback.format_exc()}")
                yield json.dumps({
                    "type": "error",
                    "message": f"生成SQL时发生错误: {str(e)}"
                }) + "\n"

        except Exception as e:
            traceback.print_exc()
            yield json.dumps({
                "type": "error",
                "message": str(e)
            }) + "\n"

    async def execute_sql(self, sql: str, db_connection) -> dict:
        """
        执行SQL查询并返回结果
        """
        try:
            result = db_connection.execute(sql)
            columns = result.keys()
            data = [dict(zip(columns, row)) for row in result.fetchall()]
            return {
                "status": "success",
                "data": data,
                "columns": columns
            }
        except Exception as e:
            traceback.print_exc()
            return {
                "status": "error",
                "message": str(e)
            }