178 lines
6.1 KiB
Python
178 lines
6.1 KiB
Python
import asyncio
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import json
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from openai import OpenAI
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from sci_mcp import *
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tools_schemas = get_domain_tool_schemas(["material",'general'])
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tool_map = get_domain_tools(["material",'general'])
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api_key="gpustack_41f8963fb74e9f39_931a02e3ab35b41b1eada85da9c40705"
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base_url="http://gpustack.ddwtop.team/v1-openai"
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client = OpenAI(
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api_key=api_key,
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base_url=base_url,
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)
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messages = [ {"role": "user", "content": """data_Ti4V
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_symmetry_space_group_name_H-M Fmmm
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_cell_length_a 3.18353600
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_cell_length_b 4.52677200
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_cell_length_c 22.74397000
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_cell_angle_alpha 90.00000000
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_cell_angle_beta 90.00000000
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_cell_angle_gamma 90.00000000
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_symmetry_Int_Tables_number 69
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_chemical_formula_structural Ti4V
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_chemical_formula_sum 'Ti16 V4'
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_cell_volume 327.76657340
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_cell_formula_units_Z 4
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loop_
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_symmetry_equiv_pos_site_id
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_symmetry_equiv_pos_as_xyz
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1 'x, y, z'
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2 '-x, -y, -z'
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3 '-x, -y, z'
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4 'x, y, -z'
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5 'x, -y, -z'
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6 '-x, y, z'
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7 '-x, y, -z'
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8 'x, -y, z'
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9 'x+1/2, y, z+1/2'
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10 '-x+1/2, -y, -z+1/2'
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11 '-x+1/2, -y, z+1/2'
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12 'x+1/2, y, -z+1/2'
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13 'x+1/2, -y, -z+1/2'
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14 '-x+1/2, y, z+1/2'
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15 '-x+1/2, y, -z+1/2'
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16 'x+1/2, -y, z+1/2'
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17 'x+1/2, y+1/2, z'
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18 '-x+1/2, -y+1/2, -z'
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19 '-x+1/2, -y+1/2, z'
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20 'x+1/2, y+1/2, -z'
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21 'x+1/2, -y+1/2, -z'
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22 '-x+1/2, y+1/2, z'
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23 '-x+1/2, y+1/2, -z'
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24 'x+1/2, -y+1/2, z'
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25 'x, y+1/2, z+1/2'
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26 '-x, -y+1/2, -z+1/2'
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27 '-x, -y+1/2, z+1/2'
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28 'x, y+1/2, -z+1/2'
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29 'x, -y+1/2, -z+1/2'
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30 '-x, y+1/2, z+1/2'
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31 '-x, y+1/2, -z+1/2'
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32 'x, -y+1/2, z+1/2'
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loop_
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_atom_site_type_symbol
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_atom_site_label
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_atom_site_symmetry_multiplicity
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_atom_site_fract_x
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_atom_site_fract_y
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_atom_site_fract_z
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_atom_site_occupancy
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Ti Ti0 8 0.00000000 0.00000000 0.19939400 1.0
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Ti Ti1 8 0.00000000 0.00000000 0.40878900 1.0
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V V2 4 0.00000000 0.00000000 0.00000000 1.0
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，根据上文提供的CIF文件，请你分析该晶体材料的化学式、晶体系统、空间群、密度、是否为金属、磁性和稳定性，并用JSON格式回答。"""}]
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def get_t1_response(messages):
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completion = client.chat.completions.create(
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model="MARS-T1",
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messages=messages,
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temperature=0.3,
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tools=tools_schemas,
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)
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choice = completion.choices[0]
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reasoning_content = choice.message.content
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#print("Reasoning content:", reasoning_content)
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if hasattr(choice.message, 'tool_calls') and choice.message.tool_calls:
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tool_calls_list = []
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for tool_call in choice.message.tool_calls:
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tool_call_dict = {
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"id": tool_call.id,
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"type": "function",
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"function": {
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"name": tool_call.function.name,
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"arguments": tool_call.function.arguments
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}
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}
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tool_calls_list.append(tool_call_dict)
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return reasoning_content, tool_calls_list
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async def execute_tool(tool_name,tool_arguments):
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try:
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tool_func = tool_map[tool_name] # 获取工具函数
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arguments = {}
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if tool_arguments:
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# 检查arguments是字符串还是字典
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if isinstance(tool_arguments, dict):
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# 如果已经是字典，直接使用
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arguments = tool_arguments
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elif isinstance(tool_arguments, str):
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# 如果是字符串，尝试解析为JSON
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try:
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# 尝试直接解析为JSON对象
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arguments = json.loads(tool_arguments)
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except json.JSONDecodeError:
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# 如果解析失败，可能是因为字符串中包含转义字符
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# 尝试修复常见的JSON字符串问题
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fixed_str = tool_arguments.replace('\\"', '"').replace('\\\\', '\\')
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try:
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arguments = json.loads(fixed_str)
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except json.JSONDecodeError:
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# 如果仍然失败，尝试将字符串作为原始字符串处理
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arguments = {"raw_string": tool_arguments}
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# 调用工具函数
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if asyncio.iscoroutinefunction(tool_func):
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# 如果是异步函数，使用await调用
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result = await tool_func(**arguments)
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else:
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# 如果是同步函数，直接调用
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result = tool_func(**arguments)
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# if func_name=='generate_material':
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# print("xxxxx",result)
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return result
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finally:
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# 清除LLM调用上下文标记
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pass
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def get_all_tool_calls_results(tool_calls_list):
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all_results = []
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for tool_call in tool_calls_list:
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tool_name = tool_call['function']['name']
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tool_arguments = tool_call['function']['arguments']
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result = asyncio.run(execute_tool(tool_name,tool_arguments))
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result_str = f"[{tool_name} content begin]\n"+result+f"\n[{tool_name} content end]\n"
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all_results.append(result_str)
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return all_results
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def get_response_from_r1(messages):
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completion = client.chat.completions.create(
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model="MARS-R1",
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messages=messages,
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temperature=0.3,
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)
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choice = completion.choices[0]
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return choice.message.content
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print("R1 RESPONSE:", choice.message.content)
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if __name__ == '__main__':
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reasoning_content, tool_calls_list=get_t1_response(messages)
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print("Reasoning content:", reasoning_content)
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tool_call_results=get_all_tool_calls_results(tool_calls_list)
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tool_call_results_str = "\n".join(tool_call_results)
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# for tool_call in tool_call_results:
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# print(tool_call)
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user_message = {
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"role": "user",
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"content": f"# 信息如下:{tool_call_results_str}# 问题如下 {messages[0]['content']}"
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}
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print("user_message_for_r1:", user_message)
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get_response_from_r1([user_message])
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