#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File: ModelTool.py
Date: 2025-05-15
Author: oyyz
Description: 模型工具类
"""

from openai import OpenAI
import httpx
import time
import logging  # 导入 logging 模块
from langchain.embeddings.base import Embeddings
from typing import List, Any
import requests
import os
import logging
from rag2_0.tool.APIKeyManager import APIKeyManager

class SiliconFlowEmbeddings(Embeddings):
    """SiliconFlow嵌入模型封装"""
    def __init__(self, api_key: str, model: str = "bge-m3"):
        self.api_key = api_key
        self.model = model
        self.url = "http://10.1.16.39:9995/v1/embeddings"
        self.headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }

    def _embed(self, input: List[str]) -> List[List[float]]:
        payload = {
            "model": self.model,
            "input": input,
            "encoding_format": "float"
        }
        response = requests.post(self.url, json=payload, headers=self.headers)
        response.raise_for_status()
        data = response.json()
        return [item["embedding"] for item in data["data"]]

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        return self._embed(texts)

    def embed_query(self, text: str) -> List[float]:
        return self._embed([text])[0]

class SiliconFlowReRankerModel:    
    @staticmethod
    def rerank(query: str, documents: List[str], top_k: int = 10) -> List[str]:
        """
        使用硅流重排模型对文档进行重新排序
        
        Args:
            query: 用户查询文本
            documents: 需要重新排序的文档列表
            top_k: 返回排序后的前k个文档
            
        Returns:
            List[dict]: 重排序后的文档列表，每个元素包含document内容、相关性分数和原始索引
        """
        url = "https://api.siliconflow.cn/v1/rerank"
        payload = {
            "model": "BAAI/bge-reranker-v2-m3",
            "query": query,
            "documents": documents,
            "top_n": top_k,
            "max_chunks_per_doc": 1024,
            "overlap_tokens": 80,
            "return_documents": True
        }
        api_key = APIKeyManager.get_api_key()
        headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }
        try:
            response = requests.post(url, json=payload, headers=headers)
            response.raise_for_status()
            results = response.json()
            return [{"document": item["document"]["text"], "score": item["relevance_score"], "index": item["index"]} for item in results["results"]]
        except requests.exceptions.RequestException as e:
            logging.error(f"重排序请求失败: {str(e)}")
            return []
        
class XinferenceReRankerModel:
    """重排模型封装"""

    @staticmethod
    def rerank(query: str, documents: List[str], top_k: int = 10) -> List[str]:
        """
        使用重排序模型对文档进行重新排序
        
        Args:
            query: 用户查询文本
            documents: 需要重新排序的文档列表
            top_k: 返回排序后的前k个文档
            
        Returns:
            List[dict]: 重排序后的文档列表，每个元素包含document内容、相关性分数和原始索引
        """
        url = "http://10.1.16.39:9995/v1/rerank"
        

        params = {"documents": documents, "query": query, "top_n": top_k, "return_documents": True, "model": os.getenv("RERANKER_MODEL_NAME")}
        headers = {
            "Authorization": "Bearer <token>",  # 这里需要替换为实际的token
            "Content-Type": "application/json"
        }
        
        try:
            response = requests.post(url, json=params, headers=headers)
            response.raise_for_status()  # 检查响应状态
            results = response.json()
            
            # 返回重排序后的文档列表
            return [{"document": item["document"]["text"], "score": item["relevance_score"], "index": item["index"]} for item in results["results"]]
            
        except requests.exceptions.RequestException as e:
            logging.error(f"XinferenceReRankerModel重排序请求失败: {str(e)}")
            return []



class OpenAiLLM:

    def __init__(self, **kwargs):
        if kwargs.get("api_key") == None or  kwargs.get("base_url") == None or kwargs.get("model") == None:
            raise ValueError("api_key, base_url, model 不能为空")

        self._api_key = kwargs.get("api_key")
        self._url = kwargs.get("base_url")
        self._model = kwargs.get("model")

        kwargs.pop("api_key")
        kwargs.pop("base_url")
        kwargs.pop("model")
        self._kwargs = kwargs

    def invoke(self, user_prompt="你是谁？", need_retry=True):
        # 初始化 OpenAI 客户端
        api_key = APIKeyManager.get_api_key()  
        client = OpenAI(api_key=api_key, base_url=self._url)

        max_retries = 3
        retry_count = 0
        
        if need_retry:
            while retry_count < max_retries:
                try:
                    # 创建 Completion 请求. 超时120s
                    completion = client.chat.completions.create(
                        model=self._model,
                        messages=[{'role': 'user', 'content': user_prompt}],
                        timeout=httpx.Timeout(300.0),
                        **self._kwargs
                    )
                    return completion.choices[0].message
                    
                except Exception as e:
                    retry_count += 1
                    if retry_count == max_retries:
                        logging.error(f"LLM 重试{max_retries}次后仍然失败: {e}")
                        return ""
                    else:
                        time.sleep(5*retry_count)  # 重试前等待1秒
        else:
            # 创建 Completion 请求. 超时120s
            completion = client.chat.completions.create(
                model=self._model,
                messages=[{'role': 'user', 'content': user_prompt}],
                timeout=httpx.Timeout(300.0),
                **self._kwargs
            )
            return completion.choices[0].message

if __name__ == "__main__":
    # 测试重排模型
    reranker = SiliconFlowReRankerModel()
    
    # 测试用例1：简单问题
    query = "他想做什么"
    documents = ["她想去公园跑步", "她想换一个新手机", "明天她想出去旅游"]
    results = reranker.rerank(query, documents)
    print(f"测试用例1 - 查询:{query}")
    for idx, item in enumerate(results):
        print(f"{idx+1}. 文档: {item['document']}, 分数: {item['score']}")
    print("-" * 50)
    
    # 测试用例2：技术问题
    query = "Python如何处理JSON数据"
    documents = [
        "Python中可以使用json模块来处理JSON数据，例如json.loads()将JSON字符串转换为字典",
        "Java提供了多种处理JSON的库，比如Jackson和Gson",
        "在Python中，可以使用pandas库来分析CSV数据",
        "JavaScript可以使用JSON.parse()方法解析JSON字符串"
    ]
    results = reranker.rerank(query, documents)
    print(f"测试用例2 - 查询:{query}")
    for idx, item in enumerate(results):
        print(f"{idx+1}. 文档: {item['document']}, 分数: {item['score']}")
    print("-" * 50)
    
    # 测试用例3：医疗问题
    query = "高血压的症状有哪些"
    documents = [
        "高血压的常见症状包括头痛、头晕、耳鸣和视力模糊",
        "糖尿病的症状包括多饮、多尿和体重减轻",
        "心脏病的症状通常包括胸痛、呼吸急促和疲劳",
        "高血压患者应该定期监测血压，保持健康的生活方式"
    ]
    results = reranker.rerank(query, documents)
    print(f"测试用例3 - 查询:{query}")
    for idx, item in enumerate(results):
        print(f"{idx+1}. 文档: {item['document']}, 分数: {item['score']}")
    print("-" * 50)
    
    # 测试用例4：长文本查询和文档
    query = "人工智能在医疗领域的应用及其伦理问题"
    documents = [
        "人工智能在医疗诊断中的应用已经显示出良好的效果，例如通过分析医学影像来检测疾病。然而，这也引发了关于医生角色和责任的伦理问题。",
        "在教育领域，人工智能可以提供个性化学习体验，适应不同学生的学习进度和风格。",
        "医疗伦理问题主要包括患者隐私保护、知情同意和医疗资源分配等方面。",
        "人工智能技术在金融领域的应用主要集中在风险评估、欺诈检测和算法交易等方面。"
    ]
    results = reranker.rerank(query, documents)
    print(f"测试用例4 - 查询:{query}")
    for idx, item in enumerate(results):
        print(f"{idx+1}. 文档: {item['document']}, 分数: {item['score']}")
    print("-" * 50)