233 lines
8.9 KiB
Python
233 lines
8.9 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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File: ModelTool.py
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Date: 2025-05-15
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Author: oyyz
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Description: 模型工具类
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"""
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from openai import OpenAI
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import httpx
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import time
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import logging # 导入 logging 模块
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from langchain.embeddings.base import Embeddings
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from typing import List, Any
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import requests
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import os
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import logging
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from rag2_0.tool.APIKeyManager import APIKeyManager
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class SiliconFlowEmbeddings(Embeddings):
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"""SiliconFlow嵌入模型封装"""
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def __init__(self, api_key: str, model: str = "bge-m3"):
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self.api_key = api_key
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self.model = model
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self.url = "http://10.1.16.39:9995/v1/embeddings"
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self.headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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def _embed(self, input: List[str]) -> List[List[float]]:
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payload = {
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"model": self.model,
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"input": input,
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"encoding_format": "float"
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}
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response = requests.post(self.url, json=payload, headers=self.headers)
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response.raise_for_status()
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data = response.json()
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return [item["embedding"] for item in data["data"]]
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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return self._embed(texts)
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def embed_query(self, text: str) -> List[float]:
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return self._embed([text])[0]
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class SiliconFlowReRankerModel:
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@staticmethod
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def rerank(query: str, documents: List[str], top_k: int = 10) -> List[str]:
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"""
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使用硅流重排模型对文档进行重新排序
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Args:
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query: 用户查询文本
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documents: 需要重新排序的文档列表
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top_k: 返回排序后的前k个文档
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Returns:
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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
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"""
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url = "https://api.siliconflow.cn/v1/rerank"
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payload = {
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"model": "BAAI/bge-reranker-v2-m3",
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"query": query,
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"documents": documents,
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"top_n": top_k,
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"max_chunks_per_doc": 1024,
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"overlap_tokens": 80,
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"return_documents": True
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}
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api_key = APIKeyManager.get_api_key()
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}
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try:
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response = requests.post(url, json=payload, headers=headers)
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response.raise_for_status()
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results = response.json()
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return [{"document": item["document"]["text"], "score": item["relevance_score"], "index": item["index"]} for item in results["results"]]
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except requests.exceptions.RequestException as e:
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logging.error(f"重排序请求失败: {str(e)}")
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return []
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class XinferenceReRankerModel:
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"""重排模型封装"""
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@staticmethod
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def rerank(query: str, documents: List[str], top_k: int = 10) -> List[str]:
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"""
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使用重排序模型对文档进行重新排序
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Args:
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query: 用户查询文本
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documents: 需要重新排序的文档列表
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top_k: 返回排序后的前k个文档
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Returns:
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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
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"""
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url = "http://10.1.16.39:9995/v1/rerank"
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params = {"documents": documents, "query": query, "top_n": top_k, "return_documents": True, "model": os.getenv("RERANKER_MODEL_NAME")}
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headers = {
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"Authorization": "Bearer <token>", # 这里需要替换为实际的token
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"Content-Type": "application/json"
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}
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try:
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response = requests.post(url, json=params, headers=headers)
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response.raise_for_status() # 检查响应状态
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results = response.json()
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# 返回重排序后的文档列表
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return [{"document": item["document"]["text"], "score": item["relevance_score"], "index": item["index"]} for item in results["results"]]
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except requests.exceptions.RequestException as e:
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logging.error(f"XinferenceReRankerModel重排序请求失败: {str(e)}")
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return []
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class OpenAiLLM:
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def __init__(self, **kwargs):
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if kwargs.get("api_key") == None or kwargs.get("base_url") == None or kwargs.get("model") == None:
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raise ValueError("api_key, base_url, model 不能为空")
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self._api_key = kwargs.get("api_key")
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self._url = kwargs.get("base_url")
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self._model = kwargs.get("model")
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kwargs.pop("api_key")
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kwargs.pop("base_url")
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kwargs.pop("model")
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self._kwargs = kwargs
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def invoke(self, user_prompt="你是谁？", need_retry=True):
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# 初始化 OpenAI 客户端
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api_key = APIKeyManager.get_api_key()
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client = OpenAI(api_key=api_key, base_url=self._url)
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max_retries = 3
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retry_count = 0
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if need_retry:
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while retry_count < max_retries:
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try:
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# 创建 Completion 请求. 超时120s
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completion = client.chat.completions.create(
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model=self._model,
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messages=[{'role': 'user', 'content': user_prompt}],
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timeout=httpx.Timeout(300.0),
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**self._kwargs
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)
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return completion.choices[0].message
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except Exception as e:
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retry_count += 1
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if retry_count == max_retries:
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logging.error(f"LLM 重试{max_retries}次后仍然失败: {e}")
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return ""
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else:
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time.sleep(5*retry_count) # 重试前等待1秒
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else:
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# 创建 Completion 请求. 超时120s
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completion = client.chat.completions.create(
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model=self._model,
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messages=[{'role': 'user', 'content': user_prompt}],
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timeout=httpx.Timeout(300.0),
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**self._kwargs
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)
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return completion.choices[0].message
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if __name__ == "__main__":
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# 测试重排模型
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reranker = SiliconFlowReRankerModel()
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# 测试用例1：简单问题
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query = "他想做什么"
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documents = ["她想去公园跑步", "她想换一个新手机", "明天她想出去旅游"]
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results = reranker.rerank(query, documents)
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print(f"测试用例1 - 查询:{query}")
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for idx, item in enumerate(results):
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print(f"{idx+1}. 文档: {item['document']}, 分数: {item['score']}")
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print("-" * 50)
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# 测试用例2：技术问题
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query = "Python如何处理JSON数据"
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documents = [
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"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",
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"Java提供了多种处理JSON的库，比如Jackson和Gson",
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"在Python中，可以使用pandas库来分析CSV数据",
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"JavaScript可以使用JSON.parse()方法解析JSON字符串"
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]
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results = reranker.rerank(query, documents)
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print(f"测试用例2 - 查询:{query}")
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for idx, item in enumerate(results):
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print(f"{idx+1}. 文档: {item['document']}, 分数: {item['score']}")
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print("-" * 50)
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# 测试用例3：医疗问题
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query = "高血压的症状有哪些"
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documents = [
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"高血压的常见症状包括头痛、头晕、耳鸣和视力模糊",
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"糖尿病的症状包括多饮、多尿和体重减轻",
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"心脏病的症状通常包括胸痛、呼吸急促和疲劳",
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"高血压患者应该定期监测血压，保持健康的生活方式"
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]
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results = reranker.rerank(query, documents)
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print(f"测试用例3 - 查询:{query}")
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for idx, item in enumerate(results):
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print(f"{idx+1}. 文档: {item['document']}, 分数: {item['score']}")
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print("-" * 50)
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# 测试用例4：长文本查询和文档
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query = "人工智能在医疗领域的应用及其伦理问题"
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documents = [
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"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。",
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"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。",
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"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。",
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"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。"
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]
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results = reranker.rerank(query, documents)
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print(f"测试用例4 - 查询:{query}")
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for idx, item in enumerate(results):
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print(f"{idx+1}. 文档: {item['document']}, 分数: {item['score']}")
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print("-" * 50)
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