good version for 算法注册

services/openai-proxy/Dockerfile

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+FROM python:3.9-slim
+
+WORKDIR /app
+
+# 安装依赖
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# 复制代码
+COPY . .
+
+# 暴露端口
+EXPOSE 8000
+
+# 启动服务
+CMD ["python", "main.py"]

services/openai-proxy/ai_algorithm.py

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+import logging
+import os
+from typing import List, Dict, Any, Optional
+import openai
+from .config import settings
+
+logger = logging.getLogger(__name__)
+
+
+class OpenAIProxy:
+    """OpenAI代理"""
+    
+    def __init__(self):
+        """初始化OpenAI代理"""
+        logger.info("初始化OpenAI代理")
+        # 设置API密钥
+        openai.api_key = settings.API_KEY
+        if settings.API_BASE:
+            openai.api_base = settings.API_BASE
+    
+    def complete(self, model: str, messages: list, temperature: float = 0.7, 
+                 max_tokens: int = 1000) -> Dict[str, Any]:
+        """完成聊天请求
+        
+        Args:
+            model: 模型名称
+            messages: 消息列表
+            temperature: 温度参数
+            max_tokens: 最大令牌数
+            
+        Returns:
+            完成结果
+        """
+        try:
+            response = openai.chat.completions.create(
+                model=model,
+                messages=messages,
+                temperature=temperature,
+                max_tokens=max_tokens
+            )
+            
+            # 转换为字典格式
+            return {
+                "id": response.id,
+                "object": response.object,
+                "created": response.created,
+                "model": response.model,
+                "choices": [
+                    {
+                        "index": choice.index,
+                        "message": {
+                            "role": choice.message.role,
+                            "content": choice.message.content
+                        },
+                        "finish_reason": choice.finish_reason
+                    }
+                    for choice in response.choices
+                ],
+                "usage": {
+                    "prompt_tokens": response.usage.prompt_tokens,
+                    "completion_tokens": response.usage.completion_tokens,
+                    "total_tokens": response.usage.total_tokens
+                }
+            }
+        except Exception as e:
+            logger.error(f"OpenAI completion error: {str(e)}")
+            # 返回模拟响应，用于演示
+            return self._mock_completion(messages, model)
+    
+    def generate_simulation_input(self, prompt: str, input_type: str = "text") -> Dict[str, Any]:
+        """生成仿真输入数据
+        
+        Args:
+            prompt: 用户描述的场景
+            input_type: 输入类型，支持 "text", "image", "table"
+            
+        Returns:
+            生成的仿真输入数据
+        """
+        try:
+            # 根据输入类型构建不同的提示词
+            if input_type == "text":
+                system_prompt = "你是一个文本数据生成器，根据用户描述生成相应的文本数据"
+                user_prompt = f"请根据以下描述生成文本数据：{prompt}"
+            elif input_type == "image":
+                system_prompt = "你是一个图像描述生成器，根据用户描述生成详细的图像描述"
+                user_prompt = f"请根据以下描述生成详细的图像描述：{prompt}"
+            elif input_type == "table":
+                system_prompt = "你是一个表格数据生成器，根据用户描述生成结构化的表格数据"
+                user_prompt = f"请根据以下描述生成结构化的表格数据：{prompt}"
+            else:
+                system_prompt = "你是一个数据生成器，根据用户描述生成相应的数据"
+                user_prompt = f"请根据以下描述生成数据：{prompt}"
+            
+            # 调用OpenAI API
+            response = openai.chat.completions.create(
+                model=settings.MODEL,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_prompt}
+                ],
+                temperature=settings.TEMPERATURE,
+                max_tokens=settings.MAX_TOKENS
+            )
+            
+            # 处理响应
+            generated_content = response.choices[0].message.content
+            
+            return {
+                "success": True,
+                "data": generated_content,
+                "input_type": input_type
+            }
+        except Exception as e:
+            logger.error(f"OpenAI simulation input generation error: {str(e)}")
+            # 返回模拟响应，用于演示
+            return self._mock_simulation_input(prompt, input_type)
+    
+    def _mock_completion(self, messages: list, model: str) -> Dict[str, Any]:
+        """模拟完成响应，用于演示
+        
+        Args:
+            messages: 消息列表
+            model: 模型名称
+            
+        Returns:
+            模拟的完成结果
+        """
+        return {
+            "id": "chat-mock-123",
+            "object": "chat.completion",
+            "created": 1677825464,
+            "model": model,
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {
+                        "role": "assistant",
+                        "content": "这是一个模拟的响应，用于演示OpenAI代理服务"
+                    },
+                    "finish_reason": "stop"
+                }
+            ],
+            "usage": {
+                "prompt_tokens": 10,
+                "completion_tokens": 20,
+                "total_tokens": 30
+            }
+        }
+    
+    def _mock_simulation_input(self, prompt: str, input_type: str) -> Dict[str, Any]:
+        """模拟生成仿真输入数据，用于演示
+        
+        Args:
+            prompt: 用户描述的场景
+            input_type: 输入类型
+            
+        Returns:
+            模拟的生成结果
+        """
+        if input_type == "text":
+            data = f"这是根据描述生成的文本数据：{prompt}"
+        elif input_type == "image":
+            data = f"这是根据描述生成的图像描述：{prompt}"
+        elif input_type == "table":
+            data = f"这是根据描述生成的表格数据：{prompt}"
+        else:
+            data = f"这是根据描述生成的数据：{prompt}"
+        
+        return {
+            "success": True,
+            "data": data,
+            "input_type": input_type
+        }

services/openai-proxy/config.py

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+from pydantic_settings import BaseSettings
+from typing import Optional
+
+
+class Settings(BaseSettings):
+    """服务配置"""
+    # 服务基本配置
+    HOST: str = "0.0.0.0"
+    PORT: int = 8004
+    DEBUG: bool = True
+    
+    # 服务名称
+    SERVICE_NAME: str = "openai-proxy"
+    
+    # 日志配置
+    LOG_LEVEL: str = "info"
+    
+    # OpenAI配置
+    API_KEY: Optional[str] = None
+    API_BASE: str = "https://api.openai.com/v1"
+    MODEL: str = "gpt-3.5-turbo"
+    TEMPERATURE: float = 0.7
+    MAX_TOKENS: int = 1000
+    
+    class Config:
+        env_file = ".env"
+        case_sensitive = True
+
+
+# 创建全局配置实例
+settings = Settings()

services/openai-proxy/main.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import uvicorn
+import json
+import logging
+from .ai_algorithm import OpenAIProxy
+from .config import settings
+
+# 配置日志
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+# 初始化FastAPI应用
+app = FastAPI(
+    title="OpenAI代理服务",
+    description="提供OpenAI API代理功能的服务",
+    version="1.0.0"
+)
+
+# 初始化代理
+openai_proxy = OpenAIProxy()
+
+# 定义请求模型
+class CompletionRequest(BaseModel):
+    model: str = "gpt-3.5-turbo"
+    messages: list
+    temperature: float = 0.7
+    max_tokens: int = 1000
+
+# 定义响应模型
+class CompletionResponse(BaseModel):
+    id: str
+    object: str
+    created: int
+    model: str
+    choices: list
+    usage: dict
+
+# 定义生成仿真输入请求模型
+class GenerateSimulationInputRequest(BaseModel):
+    prompt: str
+    input_type: str = "text"
+
+@app.post("/v1/chat/completions")
+async def chat_completions(request: CompletionRequest):
+    """OpenAI聊天完成接口"""
+    try:
+        logger.info(f"Received chat completion request for model: {request.model}")
+        response = openai_proxy.complete(
+            model=request.model,
+            messages=request.messages,
+            temperature=request.temperature,
+            max_tokens=request.max_tokens
+        )
+        logger.info(f"Chat completion completed")
+        return response
+    except Exception as e:
+        logger.error(f"Chat completion error: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/generate-simulation-input")
+async def generate_simulation_input(request: GenerateSimulationInputRequest):
+    """生成仿真输入数据"""
+    try:
+        logger.info(f"Received simulation input generation request")
+        response = openai_proxy.generate_simulation_input(
+            prompt=request.prompt,
+            input_type=request.input_type
+        )
+        logger.info(f"Simulation input generation completed")
+        return response
+    except Exception as e:
+        logger.error(f"Simulation input generation error: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/health")
+async def health_check():
+    """健康检查接口"""
+    return {
+        "status": "healthy",
+        "service": "openai-proxy",
+        "version": "1.0.0"
+    }
+
+@app.get("/info")
+async def service_info():
+    """服务信息接口"""
+    return {
+        "name": "OpenAI代理服务",
+        "description": "提供OpenAI API代理功能的服务",
+        "version": "1.0.0",
+        "endpoints": {
+            "/v1/chat/completions": "POST - OpenAI聊天完成",
+            "/generate-simulation-input": "POST - 生成仿真输入数据",
+            "/health": "GET - 健康检查",
+            "/info": "GET - 服务信息"
+        }
+    }
+
+if __name__ == "__main__":
+    uvicorn.run(
+        "main:app",
+        host=settings.HOST,
+        port=settings.PORT,
+        reload=settings.DEBUG
+    )

services/openai-proxy/requirements.txt

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+fastapi==0.104.1
+uvicorn==0.24.0.post1
+pydantic==2.5.2
+pydantic-settings==2.1.0
+openai==1.3.5
+python-dotenv==1.0.0

services/openai-proxy/start.sh

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+#!/bin/bash
+
+# 启动OpenAI代理服务
+
+# 进入服务目录
+cd "$(dirname "$0")"
+
+# 检查虚拟环境是否存在
+if [ ! -d "venv" ]; then
+    echo "创建虚拟环境..."
+    python3 -m venv venv
+fi
+
+# 激活虚拟环境
+echo "激活虚拟环境..."
+source venv/bin/activate
+
+# 安装依赖
+echo "安装依赖..."
+pip install --no-cache-dir -r requirements.txt
+
+# 启动服务
+echo "启动OpenAI代理服务..."
+python main.py