import requests
from pydantic import Field
from langchain_core.language_models import LLM
from langchain_core.outputs import LLMResult, Generation
from langchain_experimental.sql import SQLDatabaseChain
from langchain_community.utilities import SQLDatabase
from server.conf import DATABASES
from serializers import CustomLLMrequestSerializer
from rest_framework.views import APIView
from urllib.parse import quote_plus
# fastapi
from fastapi import FastAPI
from pydantic import BaseModel


db_conf = DATABASES['default']
# 密码需要 URL 编码（因为有特殊字符如 @）
password_encodeed = quote_plus(db_conf['password'])

db = SQLDatabase.from_uri(f"postgresql+psycopg2://{db_conf['user']}:{password_encodeed}@{db_conf['host']}/{db_conf['name']}", include_tables=["enm_mpoint", "enm_mpointstat"])
# model_url = "http://14.22.88.72:11025/v1/chat/completions"
model_url = "http://139.159.180.64:11434/v1/chat/completions"

class CustomLLM(LLM):
    model_url: str
    def _call(self, prompt: str, stop: list = None) -> str:
        data = {
            "model": "glm4",
            "messages": [
                {
                    "role": "system",
                    "content": "你是一个 SQL 助手，严格遵循以下规则：\n"
                                "1. 只返回 PostgreSQL 语法 SQL 语句。\n"
                                "2. 严格禁止添加任何解释、注释、Markdown 代码块标记（包括 ```sql 和 ```）。\n"
                                "3. 输出必须是纯 SQL，且可直接执行，无需任何额外处理。"
                                "4. 在 SQL 中如有多个表，请始终使用表名前缀引用字段，避免字段歧义。"
                },
                {"role": "user", "content": prompt}
            ],
            "stream": False
        }
        response = requests.post(self.model_url, json=data, timeout=600)
        response.raise_for_status()
        content = response.json()["choices"][0]["message"]["content"]
        clean_sql = self.strip_sql_markdown(content)
        return clean_sql

    def _generate(self, prompts: list, stop: list = None) -> LLMResult:
        generations = []
        for prompt in prompts:
            text = self._call(prompt, stop)
            generations.append([Generation(text=text)])
        return LLMResult(generations=generations)
    
    def strip_sql_markdown(self, content: str) -> str:
        import re
        # 去掉包裹在 ```sql 或 ``` 中的内容
        match = re.search(r"```sql\s*(.*?)```", content, re.DOTALL | re.IGNORECASE)
        if match:
            return match.group(1).strip()
        match = re.search(r"```\s*(.*?)```", content, re.DOTALL)
        if match:
            return match.group(1).strip()
        return content.strip()
    
    @property
    def _llm_type(self) -> str:
        return "custom_llm"
    

class QueryLLMview(APIView):
    def post(self, request):
        serializer = CustomLLMrequestSerializer(data=request.data)
        serializer.is_valid(raise_exception=True)
        prompt = serializer.validated_data['prompt']
        llm = CustomLLM(model_url=model_url)
        chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)
        result = chain.invoke(prompt)
        return result