356 lines
13 KiB
Python
356 lines
13 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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OpenAI兼容接口到阿里云百炼平台智能体应用的转发服务
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"""
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import os
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import json
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import logging
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from http.server import HTTPServer, BaseHTTPRequestHandler
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from urllib.parse import urlparse
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import requests
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import threading
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import time
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import uuid
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from dotenv import load_dotenv
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# 尝试加载.env文件
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load_dotenv()
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler("proxy.log", encoding='utf-8'),
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logging.StreamHandler()
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]
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)
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logger = logging.getLogger(__name__)
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# 从环境变量获取配置
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DASHSCOPE_API_KEY = os.getenv("DASHSCOPE_API_KEY")
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APP_ID = os.getenv("DASHSCOPE_APP_ID")
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# 检查必要配置
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if not DASHSCOPE_API_KEY:
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raise ValueError("请设置环境变量 DASHSCOPE_API_KEY")
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if not APP_ID:
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raise ValueError("请设置环境变量 DASHSCOPE_APP_ID")
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# 阿里云百炼平台智能体应用的URL
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DASHSCOPE_APP_URL = f"https://dashscope.aliyuncs.com/api/v1/apps/{APP_ID}/completion"
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class OpenAIProxyHandler(BaseHTTPRequestHandler):
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"""处理OpenAI兼容请求并转发到阿里云百炼平台"""
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def log_message(self, format, *args):
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"""重写日志方法，使用我们的日志配置"""
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logger.info("%s - - [%s] %s" % (self.address_string(), self.log_date_time_string(), format % args))
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def do_POST(self):
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"""处理POST请求"""
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try:
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# 解析请求路径
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parsed_path = urlparse(self.path)
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# 只处理聊天完成接口
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if parsed_path.path == "/v1/chat/completions":
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self.handle_chat_completions()
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else:
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self.send_error(404, "接口未找到")
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except Exception as e:
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logger.error(f"处理POST请求时出错: {e}")
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try:
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self.send_error(500, "内部服务器错误")
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except:
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pass # 客户端可能已经断开连接
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def do_GET(self):
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"""处理GET请求"""
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try:
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# 解析请求路径
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parsed_path = urlparse(self.path)
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# 处理模型列表请求
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if parsed_path.path == "/v1/models":
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self.handle_list_models()
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else:
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self.send_error(404, "接口未找到")
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except Exception as e:
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logger.error(f"处理GET请求时出错: {e}")
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try:
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self.send_error(500, "内部服务器错误")
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except:
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pass # 客户端可能已经断开连接
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def handle_list_models(self):
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"""处理模型列表请求"""
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try:
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# 构造OpenAI兼容的模型列表响应
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models_response = {
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"object": "list",
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"data": [
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{
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"id": "dashscope-app",
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"object": "model",
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"created": int(time.time()),
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"owned_by": "dashscope"
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}
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]
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}
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# 发送响应
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self.send_response(200)
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self.send_header('Content-Type', 'application/json')
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self.send_header('Access-Control-Allow-Origin', '*')
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self.end_headers()
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response_json = json.dumps(models_response, ensure_ascii=False)
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self.wfile.write(response_json.encode('utf-8'))
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except Exception as e:
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logger.error(f"处理模型列表请求时出错: {e}")
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try:
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self.send_error(500, "内部服务器错误")
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except:
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pass # 客户端可能已经断开连接
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def handle_chat_completions(self):
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"""处理聊天完成请求"""
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try:
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# 读取请求内容
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content_length = int(self.headers.get('Content-Length', 0))
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post_data = self.rfile.read(content_length)
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# 解析JSON数据
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request_data = json.loads(post_data.decode('utf-8'))
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# 记录接收到的请求
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logger.info(f"收到OpenAI兼容请求: {json.dumps(request_data, ensure_ascii=False)}")
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# 提取用户消息
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messages = request_data.get('messages', [])
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if not messages:
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self.send_error(400, "缺少消息内容")
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return
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# 获取最后一条用户消息作为提示词
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prompt = ""
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conversation_history = []
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for message in messages:
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role = message.get('role')
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content = message.get('content', '')
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conversation_history.append(f"{role}: {content}")
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if role == 'user':
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prompt = content
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if not prompt:
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self.send_error(400, "未找到用户消息")
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return
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# 记录提取的提示词和对话历史
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logger.info(f"提取的用户提示词: {prompt}")
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logger.info(f"完整对话历史: {' | '.join(conversation_history)}")
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# 构造阿里云百炼平台请求
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dashscope_request = {
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"input": {
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"prompt": prompt
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},
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"parameters": {},
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"debug": {}
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}
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# 处理流式请求
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stream = request_data.get('stream', False)
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# 设置请求头
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headers = {
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"Authorization": f"Bearer {DASHSCOPE_API_KEY}",
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"Content-Type": "application/json"
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}
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if stream:
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headers["Accept"] = "text/event-stream"
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headers["X-DashScope-SSE"] = "enable"
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dashscope_request["parameters"]["stream"] = True
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dashscope_request["parameters"]["incremental_output"] = True
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# 记录发送给阿里云百炼平台的请求
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logger.info(f"发送到阿里云百炼平台的请求: {json.dumps(dashscope_request, ensure_ascii=False)}")
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# 转发请求到阿里云百炼平台
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response = requests.post(
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DASHSCOPE_APP_URL,
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headers=headers,
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json=dashscope_request,
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stream=stream
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)
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# 记录阿里云百炼平台的响应状态
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logger.info(f"阿里云百炼平台响应状态: {response.status_code}")
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# 设置响应头
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self.send_response(response.status_code)
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# 复制响应头
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for key, value in response.headers.items():
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# 过滤掉一些不需要的头
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if key.lower() not in ['connection', 'transfer-encoding']:
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self.send_header(key, value)
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self.end_headers()
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# 转发响应内容
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if stream:
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logger.info("处理流式响应")
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# 流式响应
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try:
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for chunk in response.iter_content(chunk_size=1024):
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if chunk:
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self.wfile.write(chunk)
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self.wfile.flush()
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except ConnectionAbortedError:
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logger.warning("客户端中断了连接")
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else:
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# 普通响应
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# 读取响应内容
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response_content = response.text
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# 记录响应内容
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logger.info(f"阿里云百炼平台响应内容: {response_content[:200]}...") # 只记录前200个字符
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# 尝试解析并转换为OpenAI格式
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try:
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dashscope_response = json.loads(response_content)
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openai_response = self.convert_to_openai_format(dashscope_response, request_data)
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response_json = json.dumps(openai_response, ensure_ascii=False)
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logger.info(f"转换后的OpenAI格式响应: {response_json[:200]}...") # 只记录前200个字符
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self.wfile.write(response_json.encode('utf-8'))
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except json.JSONDecodeError:
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# 如果不是JSON格式，构造一个标准的OpenAI格式响应
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logger.warning("响应不是JSON格式，构造标准OpenAI格式响应")
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openai_response = {
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"id": f"chatcmpl-{uuid.uuid4().hex}",
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"object": "chat.completion",
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"created": int(time.time()),
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"model": request_data.get("model", "dashscope-app"),
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"choices": [
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{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": response_content,
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},
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"finish_reason": "stop"
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}
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],
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"usage": {
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"prompt_tokens": -1,
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"completion_tokens": -1,
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"total_tokens": -1
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}
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}
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response_json = json.dumps(openai_response, ensure_ascii=False)
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self.wfile.write(response_json.encode('utf-8'))
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except Exception as e:
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logger.error(f"处理聊天完成请求时出错: {e}")
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try:
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self.send_error(500, "处理请求时出错")
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except:
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pass # 如果无法发送错误响应，则忽略
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def convert_to_openai_format(self, dashscope_response, request_data):
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"""将阿里云百炼平台响应转换为OpenAI格式"""
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try:
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# 提取文本内容
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text_content = dashscope_response.get("output", {}).get("text", "")
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finish_reason = dashscope_response.get("output", {}).get("finish_reason", "stop")
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# 构造OpenAI格式响应
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openai_response = {
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"id": f"chatcmpl-{uuid.uuid4().hex}",
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"object": "chat.completion",
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"created": int(time.time()),
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"model": request_data.get("model", "dashscope-app"),
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"choices": [
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{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": text_content,
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},
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"finish_reason": finish_reason
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}
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],
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"usage": {
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"prompt_tokens": -1,
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"completion_tokens": -1,
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"total_tokens": -1
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}
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}
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# 如果有使用情况信息，添加到响应中
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if "usage" in dashscope_response:
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openai_response["usage"] = dashscope_response["usage"]
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return openai_response
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except Exception as e:
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logger.error(f"转换响应格式时出错: {e}")
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# 如果转换失败，构造标准OpenAI格式响应
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openai_response = {
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"id": f"chatcmpl-{uuid.uuid4().hex}",
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"object": "chat.completion",
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"created": int(time.time()),
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"model": request_data.get("model", "dashscope-app"),
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"choices": [
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{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": str(dashscope_response),
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},
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"finish_reason": "stop"
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}
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],
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"usage": {
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"prompt_tokens": -1,
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"completion_tokens": -1,
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"total_tokens": -1
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}
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}
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return openai_response
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def do_OPTIONS(self):
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"""处理CORS预检请求"""
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self.send_response(200)
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self.send_header('Access-Control-Allow-Origin', '*')
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self.send_header('Access-Control-Allow-Methods', 'POST, GET, OPTIONS')
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self.send_header('Access-Control-Allow-Headers', '*')
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self.end_headers()
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def run_server(port=8000):
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"""启动HTTP服务器"""
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server_address = ('', port)
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httpd = HTTPServer(server_address, OpenAIProxyHandler)
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logger.info(f"OpenAI到阿里云百炼平台转发服务启动，监听端口 {port}")
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logger.info(f"Base URL: http://localhost:{port}/v1")
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logger.info(f"模型名称: dashscope-app")
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try:
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httpd.serve_forever()
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except KeyboardInterrupt:
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logger.info("服务已停止")
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httpd.server_close()
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if __name__ == '__main__':
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# 可以通过环境变量设置端口，默认为8000
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port = int(os.getenv("PROXY_PORT", 8000))
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run_server(port) |