good version for 算法注册

services/openai-proxy/ai_algorithm.py

@@ -0,0 +1,174 @@
+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
+        }