++

simple72/main.py

@@ -169,9 +169,10 @@ async def generate_alpha(request: Request):
             "user_universe": data.get('user_universe'),
             "user_delay": int(data.get('user_delay')) if data.get('user_delay') else None,
             "user_category": data.get('user_category'),
-            "user_data_type": data.get('user_data_type', 'MATRIX')
+            "user_data_type": data.get('user_data_type', 'MATRIX'),
+            "max_retries": int(data.get('max_retries', 20))
         }
-        print(f"配置已构建: LLM_model={config['LLM_model_name']}, alpha_id={config['alpha_id']}")
+        print(f"配置已构建: LLM_model={config['LLM_model_name']}, alpha_id={config['alpha_id']}, max_retries={config['max_retries']}")
 
         # 将配置写入临时 JSON 文件，供 Transformer 脚本读取
         config_path = os.path.join(transformer_dir, f'config_{task_id}.json')
@@ -191,6 +192,18 @@ async def generate_alpha(request: Request):
                 env={**os.environ, "PYTHONIOENCODING": "utf-8"}
             )
             print(f"Transformer 脚本执行完成，返回码: {process.returncode}")
+            
+            # 打印 Transformer 的输出到终端
+            if process.stdout:
+                print(f"\n{'='*50}")
+                print("Transformer 输出:")
+                print(f"{'='*50}")
+                print(process.stdout)
+            if process.stderr:
+                print(f"\n{'='*50}")
+                print("Transformer 错误:")
+                print(f"{'='*50}")
+                print(process.stderr)
 
             # 定义输出文件路径
             output_file = os.path.join(transformer_dir, 'output', 'Alpha_generated_expressions_success.json')
@@ -356,6 +369,70 @@ async def health_check():
     return {"status": "healthy", "service": "alpha-transformer"}
 
 
+@app.post("/api/test-llm")
+async def test_llm_connection(request: Request):
+    """
+    测试 LLM 连接
+    接收 LLM 配置，尝试连接并返回测试结果
+    """
+    try:
+        data = await request.json()
+        
+        api_key = data.get('llm_api_key')
+        base_url = data.get('llm_base_url')
+        model = data.get('llm_model')
+        
+        if not api_key or not base_url or not model:
+            return JSONResponse(
+                status_code=400,
+                content={"success": False, "error": "Missing required LLM configuration"}
+            )
+        
+        print(f"测试 LLM 连接: {base_url}, 模型: {model}")
+        
+        # 导入 openai
+        import openai
+        
+        # 创建客户端
+        client = openai.AsyncOpenAI(
+            api_key=api_key,
+            base_url=base_url
+        )
+        
+        # 尝试发送一个简单的请求
+        response = await client.chat.completions.create(
+            model=model,
+            messages=[
+                {"role": "user", "content": "Hello, this is a connection test. Reply with 'OK' only."}
+            ],
+            max_tokens=10,
+            timeout=30
+        )
+        
+        # 检查响应
+        if response and response.choices and len(response.choices) > 0:
+            content = response.choices[0].message.content
+            print(f"✓ LLM 连接测试成功: {content[:50]}...")
+            return JSONResponse(content={
+                "success": True,
+                "message": "LLM 连接成功",
+                "response": content[:100]
+            })
+        else:
+            return JSONResponse(
+                status_code=500,
+                content={"success": False, "error": "Empty response from LLM"}
+            )
+            
+    except Exception as e:
+        error_msg = str(e)
+        print(f"✗ LLM 连接测试失败: {error_msg}")
+        return JSONResponse(
+            status_code=500,
+            content={"success": False, "error": error_msg}
+        )
+
+
 @app.get("/api/download/{alpha_id}")
 async def download_results(alpha_id: str):
     """