生成数据：mattergen改成了同步

.gitignore

@@ -7,3 +7,5 @@ pyproject.toml
 /pretrained_models
 /mcp-python-sdk
 /.vscode
+
+/*filter_ok_questions_solutions_agent*

execute_tool_copy.py

@@ -6,6 +6,8 @@ import jsonlines
 from mars_toolkit import *
 import threading
 import uuid
+
+from mars_toolkit.compute.material_gen import generate_material
 # Create a lock for file writing
 file_lock = threading.Lock()
 from mysql.connector import pooling
@@ -180,153 +182,6 @@ async def process_retrieval_from_knowledge_base(data):
 
 
 
-async def mattergen(
-    properties=None,
-    batch_size=2,
-    num_batches=1,
-    diffusion_guidance_factor=2.0
-):
-    """
-    调用MatterGen服务生成晶体结构
-
-    Args:
-        properties: 可选的属性约束，例如{"dft_band_gap": 2.0}
-        batch_size: 每批生成的结构数量
-        num_batches: 批次数量
-        diffusion_guidance_factor: 控制生成结构与目标属性的符合程度
-
-    Returns:
-        生成的结构内容或错误信息
-    """
-    try:
-        # 导入MatterGenService
-        from mars_toolkit.services.mattergen_service import MatterGenService
-
-        # 获取MatterGenService实例
-        service = MatterGenService.get_instance()
-
-        # 使用服务生成材料
-        result = await service.generate(
-            properties=properties,
-            batch_size=batch_size,
-            num_batches=num_batches,
-            diffusion_guidance_factor=diffusion_guidance_factor
-        )
-
-        return result
-    except Exception as e:
-        import logging
-        logger = logging.getLogger(__name__)
-        logger.error(f"Error in mattergen: {e}")
-        import traceback
-        logger.error(traceback.format_exc())
-        return f"Error generating material: {str(e)}"
-
-async def generate_material(
-    url="http://localhost:8051/generate_material",
-    properties=None,
-    batch_size=2,
-    num_batches=1,
-    diffusion_guidance_factor=2.0
-):
-    """
-    调用MatterGen API生成晶体结构
-
-    Args:
-        url: API端点URL
-        properties: 可选的属性约束，例如{"dft_band_gap": 2.0}
-        batch_size: 每批生成的结构数量
-        num_batches: 批次数量
-        diffusion_guidance_factor: 控制生成结构与目标属性的符合程度
-
-    Returns:
-        生成的结构内容或错误信息
-    """
-    # 尝试使用本地MatterGen服务
-    try:
-        print("尝试使用本地MatterGen服务...")
-        result = await mattergen(
-            properties=properties,
-            batch_size=batch_size,
-            num_batches=num_batches,
-            diffusion_guidance_factor=diffusion_guidance_factor
-        )
-        if result and not result.startswith("Error"):
-            print("本地MatterGen服务生成成功!")
-            return result
-        else:
-            print(f"本地MatterGen服务生成失败，尝试使用API: {result}")
-    except Exception as e:
-        print(f"本地MatterGen服务出错，尝试使用API: {str(e)}")
-
-    # 如果本地服务失败，回退到API调用
-    # 规范化参数
-    normalized_args = normalize_material_args({
-        "properties": properties,
-        "batch_size": batch_size,
-        "num_batches": num_batches,
-        "diffusion_guidance_factor": diffusion_guidance_factor
-    })
-
-    # 构建请求负载
-    payload = {
-        "properties": normalized_args["properties"],
-        "batch_size": normalized_args["batch_size"],
-        "num_batches": normalized_args["num_batches"],
-        "diffusion_guidance_factor": normalized_args["diffusion_guidance_factor"]
-    }
-
-    print(f"发送请求到 {url}")
-    print(f"请求参数: {json.dumps(payload, ensure_ascii=False, indent=2)}")
-
-    try:
-        # 添加headers参数，包含accept头
-        headers = {
-            "Content-Type": "application/json",
-            "accept": "application/json"
-        }
-
-        # 打印完整请求信息（调试用）
-        print(f"完整请求URL: {url}")
-        print(f"请求头: {json.dumps(headers, indent=2)}")
-        print(f"请求体: {json.dumps(payload, indent=2)}")
-
-        # 禁用代理设置
-        proxies = {
-            "http": None,
-            "https": None
-        }
-
-        # 发送POST请求，添加headers参数，禁用代理，增加超时时间
-        response = requests.post(url, json=payload, headers=headers, proxies=proxies, timeout=300)
-
-        # 打印响应信息（调试用）
-        print(f"响应状态码: {response.status_code}")
-        print(f"响应头: {dict(response.headers)}")
-        print(f"响应内容: {response.text[:500]}...")  # 只打印前500个字符，避免输出过长
-
-        # 检查响应状态
-        if response.status_code == 200:
-            result = response.json()
-
-            if result["success"]:
-                print("\n生成成功!")
-                return result["content"]
-            else:
-                print(f"\n生成失败: {result['message']}")
-                return None
-        else:
-            print(f"\n请求失败，状态码: {response.status_code}")
-            print(f"响应内容: {response.text}")
-            return None
-
-    except Exception as e:
-        print(f"\n发生错误: {str(e)}")
-        print(f"错误类型: {type(e).__name__}")
-        import traceback
-        print(f"错误堆栈: {traceback.format_exc()}")
-        return None
-
 async def execute_tool_from_dict(input_dict: dict):
     """
     从字典中提取工具函数名称和参数，并执行相应的工具函数
@@ -416,14 +271,14 @@ def worker(data, output_file_path):
             print(f"{Fore.CYAN}{Style.BRIGHT}【参数】{Style.RESET_ALL} {Fore.GREEN}{arguments_data}{Style.RESET_ALL}")
 
             if func.get("name") == 'retrieval_from_knowledge_base':
-                delay_time = random.uniform(1, 5)
-
-                time.sleep(delay_time)
-                result = asyncio.run(process_retrieval_from_knowledge_base(data))
-                func_results.append({"function": func['name'], "result": result})
-                # 格式化结果
-                formatted_result = f"[{func_name} content begin]{result}[{func_name} content end]"
-                formatted_results.append(formatted_result)
+                pass
+                # delay_time = random.uniform(5, 10)
+                # time.sleep(delay_time)
+                # result = asyncio.run(process_retrieval_from_knowledge_base(data))
+                # func_results.append({"function": func['name'], "result": result})
+                # # 格式化结果
+                # formatted_result = f"[{func_name} content begin]{result}[{func_name} content end]"
+                # formatted_results.append(formatted_result)
 
             elif func.get("name") == 'generate_material':
                 # 规范化参数
@@ -438,30 +293,30 @@ def worker(data, output_file_path):
 
                     # 规范化参数
                     normalized_args = normalize_material_args(arguments_data)
-                    print(f"{Fore.CYAN}{Style.BRIGHT}【函数名】{Style.RESET_ALL} {Fore.YELLOW}{func_name}{Style.RESET_ALL}")
-                    print(f"{Fore.CYAN}{Style.BRIGHT}【原始参数】{Style.RESET_ALL} {Fore.GREEN}{json.dumps(arguments_data, ensure_ascii=False, indent=2)}{Style.RESET_ALL}")
-                    print(f"{Fore.CYAN}{Style.BRIGHT}【规范化参数】{Style.RESET_ALL} {Fore.GREEN}{json.dumps(normalized_args, ensure_ascii=False, indent=2)}{Style.RESET_ALL}")
+                    # print(f"{Fore.CYAN}{Style.BRIGHT}【函数名】{Style.RESET_ALL} {Fore.YELLOW}{func_name}{Style.RESET_ALL}")
+                    # print(f"{Fore.CYAN}{Style.BRIGHT}【原始参数】{Style.RESET_ALL} {Fore.GREEN}{json.dumps(arguments_data, ensure_ascii=False, indent=2)}{Style.RESET_ALL}")
+                    # print(f"{Fore.CYAN}{Style.BRIGHT}【规范化参数】{Style.RESET_ALL} {Fore.GREEN}{json.dumps(normalized_args, ensure_ascii=False, indent=2)}{Style.RESET_ALL}")
 
                     # 优先使用mattergen函数
                     try:
-                        output = asyncio.run(generate_material(**normalized_args))
+                        # output = asyncio.run(generate_material(**normalized_args))
+                        output = generate_material(**normalized_args)
 
                         # 添加延迟，模拟额外的工具函数调用
 
-
                         # 随机延迟5-10秒
-                        delay_time = random.uniform(5, 10)
-                        print(f"{Fore.MAGENTA}{Style.BRIGHT}正在执行额外的工具函数调用，预计需要 {delay_time:.2f} 秒...{Style.RESET_ALL}")
-                        time.sleep(delay_time)
+                        # delay_time = random.uniform(5, 10)
+                        # print(f"{Fore.MAGENTA}{Style.BRIGHT}正在执行额外的工具函数调用，预计需要 {delay_time:.2f} 秒...{Style.RESET_ALL}")
+                        # time.sleep(delay_time)
 
-                        # 模拟其他工具函数调用的日志输出
-                        print(f"{Fore.BLUE}正在分析生成的材料结构...{Style.RESET_ALL}")
-                        time.sleep(0.5)
-                        print(f"{Fore.BLUE}正在计算结构稳定性...{Style.RESET_ALL}")
-                        time.sleep(0.5)
-                        print(f"{Fore.BLUE}正在验证属性约束条件...{Style.RESET_ALL}")
-                        time.sleep(0.5)
-                        print(f"{Fore.GREEN}{Style.BRIGHT}额外的工具函数调用完成{Style.RESET_ALL}")
+                        # # 模拟其他工具函数调用的日志输出
+                        # print(f"{Fore.BLUE}正在分析生成的材料结构...{Style.RESET_ALL}")
+                        # time.sleep(0.5)
+                        # print(f"{Fore.BLUE}正在计算结构稳定性...{Style.RESET_ALL}")
+                        # time.sleep(0.5)
+                        # print(f"{Fore.BLUE}正在验证属性约束条件...{Style.RESET_ALL}")
+                        # time.sleep(0.5)
+                        # print(f"{Fore.GREEN}{Style.BRIGHT}额外的工具函数调用完成{Style.RESET_ALL}")
 
                     except Exception as e:
                         print(f"{Fore.RED}mattergen出错，尝试使用generate_material: {str(e)}{Style.RESET_ALL}")
@@ -478,14 +333,15 @@ def worker(data, output_file_path):
                     print(f"{Fore.RED}{traceback.format_exc()}{Style.RESET_ALL}")
 
             else:
-                delay_time = random.uniform(1, 5)
-                time.sleep(delay_time)
-                result = asyncio.run(execute_tool_from_dict(func))
-                func_results.append({"function": func['name'], "result": result})
-                # 格式化结果
-                func_name = func.get("name")
-                formatted_result = f"[{func_name} content begin]{result}[{func_name} content end]"
-                formatted_results.append(formatted_result)
+                # delay_time = random.uniform(5, 10)
+                # time.sleep(delay_time)
+                pass
+                # result = asyncio.run(execute_tool_from_dict(func))
+                # func_results.append({"function": func['name'], "result": result})
+                # # 格式化结果
+                # func_name = func.get("name")
+                # formatted_result = f"[{func_name} content begin]{result}[{func_name} content end]"
+                # formatted_results.append(formatted_result)
 
         # 将所有格式化后的结果连接起来
         final_result = "\n\n\n".join(formatted_results)
@@ -557,8 +413,8 @@ if __name__ == '__main__':
 
     print(len(datas))
     # print()
-    output_file = f"./filter_ok_questions_solutions_agent_{datetime.datetime.now().strftime('%Y%m%d%H%M%S')}.jsonl"
-    main(datas, output_file, max_workers=16)
+    output_file = f"./filter_ok_questions_solutions_agent_mattergen_{datetime.datetime.now().strftime('%Y%m%d%H%M%S')}.jsonl"
+    main(datas, output_file, max_workers=1)
 
     # 示例1：使用正确的JSON格式
     # argument = '{"properties": {"chemical_system": "V-Zn-O", "crystal_system": "monoclinic", "space_group": "P21/c", "volume": 207.37}, "batch_size": 1, "num_batches": 1}'

execute_tool_other_tools.py

@@ -0,0 +1,423 @@
+import json
+import asyncio
+import concurrent.futures
+
+import jsonlines
+from mars_toolkit import *
+import threading
+import uuid
+
+from mars_toolkit.compute.material_gen import generate_material
+# Create a lock for file writing
+file_lock = threading.Lock()
+from mysql.connector import pooling
+from colorama import Fore, Back, Style, init
+import time
+import random
+# 初始化colorama
+init(autoreset=True)
+
+from typing import Dict, Union, Any, Optional
+
+def normalize_material_args(arguments: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    规范化传递给generate_material函数的参数格式。
+
+    处理以下情况：
+    1. properties参数可能是字符串形式的JSON，需要解析为字典
+    2. properties中的值可能需要转换为适当的类型（数字或字符串）
+    3. 确保batch_size和num_batches是整数
+
+    Args:
+        arguments: 包含generate_material参数的字典
+
+    Returns:
+        规范化后的参数字典
+    """
+    normalized_args = arguments.copy()
+
+    # 处理properties参数
+    if "properties" in normalized_args:
+        properties = normalized_args["properties"]
+
+        # 如果properties是字符串，尝试解析为JSON
+        if isinstance(properties, str):
+            try:
+                properties = json.loads(properties)
+            except json.JSONDecodeError as e:
+                raise ValueError(f"无法解析properties JSON字符串: {e}")
+
+        # 确保properties是字典
+        if not isinstance(properties, dict):
+            raise ValueError(f"properties必须是字典或JSON字符串，而不是 {type(properties)}")
+
+        # 处理properties中的值
+        normalized_properties = {}
+        for key, value in properties.items():
+            # 处理范围值，例如 "0.0-2.0" 或 "40-50"
+            if isinstance(value, str) and "-" in value and not value.startswith(">") and not value.startswith("<"):
+                # 保持范围值为字符串格式
+                normalized_properties[key] = value
+            elif isinstance(value, str) and value.startswith(">"):
+                # 保持大于值为字符串格式
+                normalized_properties[key] = value
+            elif isinstance(value, str) and value.startswith("<"):
+                # 保持小于值为字符串格式
+                normalized_properties[key] = value
+            elif isinstance(value, str) and value.lower() == "relaxor":
+                # 特殊值保持为字符串
+                normalized_properties[key] = value
+            elif isinstance(value, str) and value.endswith("eV"):
+                # 带单位的值保持为字符串
+                normalized_properties[key] = value
+            else:
+                # 尝试将值转换为数字
+                try:
+                    # 如果可以转换为浮点数
+                    float_value = float(value)
+                    # 如果是整数，转换为整数
+                    if float_value.is_integer():
+                        normalized_properties[key] = int(float_value)
+                    else:
+                        normalized_properties[key] = float_value
+                except (ValueError, TypeError):
+                    # 如果无法转换为数字，保持原值
+                    normalized_properties[key] = value
+
+        normalized_args["properties"] = normalized_properties
+
+    # 确保batch_size和num_batches是整数
+    if "batch_size" in normalized_args:
+        try:
+            normalized_args["batch_size"] = int(normalized_args["batch_size"])
+        except (ValueError, TypeError):
+            raise ValueError(f"batch_size必须是整数，而不是 {normalized_args['batch_size']}")
+
+    if "num_batches" in normalized_args:
+        try:
+            normalized_args["num_batches"] = int(normalized_args["num_batches"])
+        except (ValueError, TypeError):
+            raise ValueError(f"num_batches必须是整数，而不是 {normalized_args['num_batches']}")
+
+    # 确保diffusion_guidance_factor是浮点数
+    if "diffusion_guidance_factor" in normalized_args:
+        try:
+            normalized_args["diffusion_guidance_factor"] = float(normalized_args["diffusion_guidance_factor"])
+        except (ValueError, TypeError):
+            raise ValueError(f"diffusion_guidance_factor必须是数字，而不是 {normalized_args['diffusion_guidance_factor']}")
+
+    return normalized_args
+
+
+import requests
+connection_pool = pooling.MySQLConnectionPool(
+        pool_name="mypool",
+        pool_size=32,
+        pool_reset_session=True,
+        host='localhost',
+        user='metadata_mat_papers',
+        password='siat-mic',
+        database='metadata_mat_papers'
+    )
+
+async def process_retrieval_from_knowledge_base(data):
+    doi = data.get('doi')
+    mp_id = data.get('mp_id')
+
+    # 检查是否提供了至少一个查询参数
+    if doi is None and mp_id is None:
+        return ""  # 如果没有提供查询参数，返回空字符串
+
+    # 构建SQL查询条件
+    query = "SELECT * FROM mp_synthesis_scheme_info WHERE "
+    params = []
+
+    if doi is not None and mp_id is not None:
+        query += "doi = %s OR mp_id = %s"
+        params = [doi, mp_id]
+    elif doi is not None:
+        query += "doi = %s"
+        params = [doi]
+    else:  # mp_id is not None
+        query += "mp_id = %s"
+        params = [mp_id]
+
+    # 从数据库中查询匹配的记录
+    conn = connection_pool.get_connection()
+    try:
+        cursor = conn.cursor(dictionary=True)
+        try:
+            cursor.execute(query, params)
+            result = cursor.fetchone()  # 获取第一个匹配的记录
+        finally:
+            cursor.close()
+    finally:
+        conn.close()
+
+    # 检查是否找到匹配的记录
+    if not result:
+        return ""  # 如果没有找到匹配记录，返回空字符串
+
+    # 构建markdown格式的结果
+    markdown_result = ""
+
+    # 添加各个字段（除了doi和mp_id）
+    fields = [
+        "target_material",
+        "reaction_string",
+        "chara_structure",
+        "chara_performance",
+        "chara_application",
+        "synthesis_schemes"
+    ]
+
+    for field in fields:
+        # 获取字段内容
+        field_content = result.get(field, "")
+        # 只有当字段内容不为空时才添加该字段
+        if field_content and field_content.strip():
+            markdown_result += f"\n## {field}\n{field_content}\n\n"
+
+    return markdown_result  # 直接返回markdown文本
+
+
+
+async def execute_tool_from_dict(input_dict: dict):
+    """
+    从字典中提取工具函数名称和参数，并执行相应的工具函数
+
+    Args:
+        input_dict: 字典，例如：
+                  {"name": "search_material_property_from_material_project",
+                   "arguments": "{\"formula\": \"Th3Pd5\", \"is_stable\": \"true\"}"}
+
+    Returns:
+        工具函数的执行结果，如果工具函数不存在则返回错误信息
+    """
+    try:
+        # 解析输入字符串为字典
+        # input_dict = json.loads(input_str)
+
+        # 提取函数名和参数
+        func_name = input_dict.get("name")
+        arguments_data = input_dict.get("arguments")
+        #print('func_name', func_name)
+        #print("argument", arguments_data)
+        if not func_name:
+            return {"status": "error", "message": "未提供函数名称"}
+
+        # 获取所有注册的工具函数
+        tools = get_tools()
+
+        # 检查函数名是否存在于工具函数字典中
+        if func_name not in tools:
+            return {"status": "error", "message": f"函数 '{func_name}' 不存在于工具函数字典中"}
+
+        # 获取对应的工具函数
+        tool_func = tools[func_name]
+
+        # 处理参数
+        arguments = {}
+        if arguments_data:
+            # 检查arguments是字符串还是字典
+            if isinstance(arguments_data, dict):
+                # 如果已经是字典，直接使用
+                arguments = arguments_data
+            elif isinstance(arguments_data, str):
+                # 如果是字符串，尝试解析为JSON
+                try:
+                    # 尝试直接解析为JSON对象
+                    arguments = json.loads(arguments_data)
+                except json.JSONDecodeError:
+                    # 如果解析失败，可能是因为字符串中包含转义字符
+                    # 尝试修复常见的JSON字符串问题
+                    fixed_str = arguments_data.replace('\\"', '"').replace('\\\\', '\\')
+                    try:
+                        arguments = json.loads(fixed_str)
+                    except json.JSONDecodeError:
+                        # 如果仍然失败，尝试将字符串作为原始字符串处理
+                        arguments = {"raw_string": arguments_data}
+
+        # 调用工具函数
+        if asyncio.iscoroutinefunction(tool_func):
+            # 如果是异步函数，使用await调用
+            result = await tool_func(**arguments)
+        else:
+            # 如果是同步函数，直接调用
+            result = tool_func(**arguments)
+        # if func_name=='generate_material':
+        #     print("xxxxx",result)
+        return result
+
+    except json.JSONDecodeError as e:
+        return {"status": "error", "message": f"JSON解析错误: {str(e)}"}
+    except Exception as e:
+        return {"status": "error", "message": f"执行过程中出错: {str(e)}"}
+
+
+def worker(data, output_file_path):
+    try:
+        func_contents = data["function_calls"]
+        func_results = []
+        formatted_results = []  # 新增一个列表来存储格式化后的结果
+        for func in func_contents:
+            func_name = func.get("name")
+            arguments_data = func.get("arguments")
+
+            # 使用富文本打印函数名
+            print(f"{Fore.CYAN}{Style.BRIGHT}【函数名】{Style.RESET_ALL} {Fore.YELLOW}{func_name}{Style.RESET_ALL}")
+
+            # 使用富文本打印参数
+            print(f"{Fore.CYAN}{Style.BRIGHT}【参数】{Style.RESET_ALL} {Fore.GREEN}{arguments_data}{Style.RESET_ALL}")
+
+            if func.get("name") == 'retrieval_from_knowledge_base':
+                pass
+                # delay_time = random.uniform(5, 10)
+                # time.sleep(delay_time)
+                result = asyncio.run(process_retrieval_from_knowledge_base(data))
+                func_results.append({"function": func['name'], "result": result})
+                # 格式化结果
+                formatted_result = f"[{func_name} content begin]{result}[{func_name} content end]"
+                formatted_results.append(formatted_result)
+
+            elif func.get("name") == 'generate_material':
+                # # 规范化参数
+                # try:
+                #     # 确保arguments_data是字典
+                #     if isinstance(arguments_data, str):
+                #         try:
+                #             arguments_data = json.loads(arguments_data)
+                #         except json.JSONDecodeError as e:
+                #             print(f"{Fore.RED}无法解析arguments_data JSON字符串: {e}{Style.RESET_ALL}")
+                #             continue
+
+                #     # 规范化参数
+                #     normalized_args = normalize_material_args(arguments_data)
+                #     # print(f"{Fore.CYAN}{Style.BRIGHT}【函数名】{Style.RESET_ALL} {Fore.YELLOW}{func_name}{Style.RESET_ALL}")
+                #     # print(f"{Fore.CYAN}{Style.BRIGHT}【原始参数】{Style.RESET_ALL} {Fore.GREEN}{json.dumps(arguments_data, ensure_ascii=False, indent=2)}{Style.RESET_ALL}")
+                #     # print(f"{Fore.CYAN}{Style.BRIGHT}【规范化参数】{Style.RESET_ALL} {Fore.GREEN}{json.dumps(normalized_args, ensure_ascii=False, indent=2)}{Style.RESET_ALL}")
+
+                #     # 优先使用mattergen函数
+                #     try:
+                #         # output = asyncio.run(generate_material(**normalized_args))
+                #         output = generate_material(**normalized_args)
+
+                #         # 添加延迟，模拟额外的工具函数调用
+
+                #         # 随机延迟5-10秒
+                #         # delay_time = random.uniform(5, 10)
+                #         # print(f"{Fore.MAGENTA}{Style.BRIGHT}正在执行额外的工具函数调用，预计需要 {delay_time:.2f} 秒...{Style.RESET_ALL}")
+                #         # time.sleep(delay_time)
+
+                #         # # 模拟其他工具函数调用的日志输出
+                #         # print(f"{Fore.BLUE}正在分析生成的材料结构...{Style.RESET_ALL}")
+                #         # time.sleep(0.5)
+                #         # print(f"{Fore.BLUE}正在计算结构稳定性...{Style.RESET_ALL}")
+                #         # time.sleep(0.5)
+                #         # print(f"{Fore.BLUE}正在验证属性约束条件...{Style.RESET_ALL}")
+                #         # time.sleep(0.5)
+                #         # print(f"{Fore.GREEN}{Style.BRIGHT}额外的工具函数调用完成{Style.RESET_ALL}")
+
+                #     except Exception as e:
+                #         print(f"{Fore.RED}mattergen出错，尝试使用generate_material: {str(e)}{Style.RESET_ALL}")
+
+                #     # 将结果添加到func_results
+                #     func_results.append({"function": func_name, "result": output})
+
+                #     # 格式化结果
+                #     formatted_result = f"[{func_name} content begin]{output}[{func_name} content end]"
+                #     formatted_results.append(formatted_result)
+                # except Exception as e:
+                #     print(f"{Fore.RED}处理generate_material参数时出错: {e}{Style.RESET_ALL}")
+                #     import traceback
+                #     print(f"{Fore.RED}{traceback.format_exc()}{Style.RESET_ALL}")
+                pass
+            else:
+                # delay_time = random.uniform(5, 10)
+                # time.sleep(delay_time)
+
+                result = asyncio.run(execute_tool_from_dict(func))
+                func_results.append({"function": func['name'], "result": result})
+                # 格式化结果
+                func_name = func.get("name")
+                formatted_result = f"[{func_name} content begin]{result}[{func_name} content end]"
+                formatted_results.append(formatted_result)
+
+        # 将所有格式化后的结果连接起来
+        final_result = "\n\n\n".join(formatted_results)
+        data['observation'] = final_result
+
+        # 使用富文本打印开始和结束标记
+        print(f"{Back.BLUE}{Fore.WHITE}{Style.BRIGHT}{'#'*50} 结果开始 {'#'*50}{Style.RESET_ALL}")
+        print(data['observation'])
+        print(f"{Back.BLUE}{Fore.WHITE}{Style.BRIGHT}{'#'*50} 结果结束 {'#'*50}{Style.RESET_ALL}")
+        with file_lock:
+            with jsonlines.open(output_file_path, mode='a') as writer:
+                writer.write(data)  # observation . data
+        return f"Processed successfully"
+
+    except Exception as e:
+        print(f"{Fore.RED}{Style.BRIGHT}处理过程中出错: {str(e)}{Style.RESET_ALL}")
+        return f"Error processing: {str(e)}"
+
+
+def main(datas, output_file_path, max_workers=1):
+    import random
+    from tqdm import tqdm
+    import os
+    from mysql.connector import pooling, Error
+
+    # 创建进度条
+    pbar = tqdm(total=len(datas), desc="Processing papers")
+
+    # 创建一个线程池
+    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+        # 提交任务到执行器
+        future_to_path = {}
+        for path in datas:
+            future = executor.submit(worker, path, output_file_path)
+            future_to_path[future] = path
+
+        # 处理结果
+        completed = 0
+        failed = 0
+        for future in concurrent.futures.as_completed(future_to_path):
+            path = future_to_path[future]
+            try:
+                result = future.result()
+                if "successfully" in result:
+                    completed += 1
+                else:
+                    failed += 1
+                # 更新进度条
+                pbar.update(1)
+                # 每100个文件更新一次统计信息
+                if (completed + failed) % 100 == 0:
+                    pbar.set_postfix(completed=completed, failed=failed)
+            except Exception as e:
+                failed += 1
+                pbar.update(1)
+                print(f"\nWorker for {path} generated an exception: {e}")
+
+    pbar.close()
+    print(f"Processing complete. Successfully processed: {completed}, Failed: {failed}")
+
+
+if __name__ == '__main__':
+    import datetime
+    import jsonlines
+    datas = []
+    with jsonlines.open('/home/ubuntu/sas0/LYT/mars1215/make_reason_src/filter_failed_questions_solutions_20250323140107.jsonl') as reader:
+        for obj in reader:
+            datas.append(obj)
+
+    print(len(datas))
+    # print()
+    output_file = f"./filter_ok_questions_solutions_agent_other_tools_{datetime.datetime.now().strftime('%Y%m%d%H%M%S')}.jsonl"
+    main(datas, output_file, max_workers=32)
+
+    # 示例1：使用正确的JSON格式
+    # argument = '{"properties": {"chemical_system": "V-Zn-O", "crystal_system": "monoclinic", "space_group": "P21/c", "volume": 207.37}, "batch_size": 1, "num_batches": 1}'
+    # argument = json.loads(argument)
+    # print(json.dumps(argument, indent=2))
+    # asyncio.run(mattergen(**argument))

mars_toolkit/compute/material_gen.py

@@ -9,9 +9,18 @@ import asyncio
 import zipfile
 import shutil
 import re
+import multiprocessing
+from multiprocessing import Process, Queue
 from pathlib import Path
 from typing import Literal, Dict, Any, Tuple, Union, Optional, List
 
+# 设置多进程启动方法为spawn，解决CUDA初始化错误
+try:
+    multiprocessing.set_start_method('spawn', force=True)
+except RuntimeError:
+    # 如果已经设置过启动方法，会抛出RuntimeError
+    pass
+
 from ase.optimize import FIRE
 from ase.filters import FrechetCellFilter
 from ase.atoms import Atoms
@@ -33,6 +42,49 @@ from ..core.mattergen_wrapper import *
 logger = logging.getLogger(__name__)
 
 
+def _process_generate_material_worker(args_queue, result_queue):
+    """
+    在新进程中处理材料生成的工作函数
+
+    Args:
+        args_queue: 包含生成参数的队列
+        result_queue: 用于返回结果的队列
+    """
+    try:
+        # 配置日志
+        import logging
+        logger = logging.getLogger(__name__)
+        logger.info("子进程开始执行材料生成...")
+
+        # 从队列获取参数
+        args = args_queue.get()
+        logger.info(f"子进程获取到参数: {args}")
+
+        # 导入MatterGenService
+        from mars_toolkit.services.mattergen_service import MatterGenService
+        logger.info("子进程成功导入MatterGenService")
+
+        # 获取MatterGenService实例
+        service = MatterGenService.get_instance()
+        logger.info("子进程成功获取MatterGenService实例")
+
+        # 使用服务生成材料
+        logger.info("子进程开始调用generate方法...")
+        result = service.generate(**args)
+        logger.info("子进程generate方法调用完成")
+
+        # 将结果放入结果队列
+        result_queue.put(result)
+        logger.info("子进程材料生成完成，结果已放入队列")
+    except Exception as e:
+        # 如果发生错误，将错误信息放入结果队列
+        import traceback
+        error_msg = f"材料生成过程中出错: {str(e)}\n{traceback.format_exc()}"
+        import logging
+        logging.getLogger(__name__).error(error_msg)
+        result_queue.put(f"Error: {error_msg}")
+
+
 def format_cif_content(content):
     """
     Format CIF content by removing unnecessary headers and organizing each CIF file.
@@ -233,7 +285,7 @@ def main(
 
 
 @llm_tool(name="generate_material", description="Generate crystal structures with optional property constraints")
-async def generate_material(
+def generate_material(
     properties: Optional[Dict[str, Union[float, str, Dict[str, Union[float, str]]]]] = None,
     batch_size: int = 2,
     num_batches: int = 1,
@@ -260,16 +312,45 @@ async def generate_material(
     Returns:
         Descriptive text with generated crystal structures in CIF format
     """
+    # # 创建队列用于进程间通信
+    # args_queue = Queue()
+    # result_queue = Queue()
+
+    # # 将参数放入队列
+    # args_queue.put({
+    #     "properties": properties,
+    #     "batch_size": batch_size,
+    #     "num_batches": num_batches,
+    #     "diffusion_guidance_factor": diffusion_guidance_factor
+    # })
+
+    # # 创建并启动新进程
+    # logger.info("启动新进程处理材料生成...")
+    # p = Process(target=_process_generate_material_worker, args=(args_queue, result_queue))
+    # p.start()
+
+    # # 等待进程完成并获取结果
+    # p.join()
+    # result = result_queue.get()
+
+    # # 检查结果是否为错误信息
+    # if isinstance(result, str) and result.startswith("Error:"):
+    #     # 记录错误日志
+    #     logger.error(result)
+
     # 导入MatterGenService
     from mars_toolkit.services.mattergen_service import MatterGenService
+    logger.info("子进程成功导入MatterGenService")
 
     # 获取MatterGenService实例
     service = MatterGenService.get_instance()
+    logger.info("子进程成功获取MatterGenService实例")
 
     # 使用服务生成材料
-    return service.generate(
-        properties=properties,
-        batch_size=batch_size,
-        num_batches=num_batches,
-        diffusion_guidance_factor=diffusion_guidance_factor
-    )
+    logger.info("子进程开始调用generate方法...")
+    result = service.generate(properties=properties, batch_size=batch_size, num_batches=num_batches, diffusion_guidance_factor=diffusion_guidance_factor)
+    logger.info("子进程generate方法调用完成")
+    if "Error generating structures" in result:
+        return f"Error: Invalid properties {properties}."
+    else:
+        return result

mars_toolkit/core/config.py

@@ -35,7 +35,7 @@ class Config:
     DIFY_API_KEY = 'app-IKZrS1RqIyurPSzR73mz6XSA'
 
     # Searxng
-    SEARXNG_HOST="http://192.168.191.101:40032/"
+    SEARXNG_HOST="http://192.168.168.1:40032/"
 
     # Visualization
     VIZ_CIF_OUTPUT_ROOT = '/home/ubuntu/50T/lzy/mars-mcp/outputs/cif_visualization'

mars_toolkit/query/web_search.py

@@ -5,6 +5,7 @@ This module provides functions for searching information on the web.
 """
 
 import asyncio
+import os
 from typing import Annotated, Dict, Any, List
 
 from langchain_community.utilities import SearxSearchWrapper
@@ -28,6 +29,8 @@ async def search_online(
         Formatted string with search results (titles, snippets, links)
     """
     # 确保 num_results 是整数
+    os.environ['HTTP_PROXY'] = ''
+    os.environ['HTTPS_PROXY'] = ''
     try:
         num_results = int(num_results)
     except (TypeError, ValueError):

test_mars_toolkit.py

@@ -62,7 +62,8 @@ async def test_tool(tool_name: str) -> str:
         elif tool_name == "generate_material":
             from mars_toolkit.compute.material_gen import generate_material
             # 使用简单的属性约束进行测试
-            result = await generate_material(properties={'dft_mag_density': 0.15}, batch_size=2, num_batches=1)
+            # result = await generate_material(properties={'dft_mag_density': 0.15}, batch_size=2, num_batches=1)
+            result = generate_material(properties={'dft_mag_density': 0.15}, batch_size=2, num_batches=1)
 
         elif tool_name == "fetch_chemical_composition_from_OQMD":
             from mars_toolkit.query.oqmd_query import fetch_chemical_composition_from_OQMD
@@ -171,7 +172,7 @@ if __name__ == "__main__":
     ]
 
     # 选择要测试的工具
-    tool_name = tools_to_test[6]  # 测试 search_online 工具
+    tool_name = tools_to_test[1]  # 测试 search_online 工具
 
     # 运行测试
     result = asyncio.run(test_tool(tool_name))