| | |
| | import re |
| | from typing import TYPE_CHECKING, List, Optional, Tuple, Union |
| |
|
| | import json |
| |
|
| | from .base import BaseAgentTemplate |
| |
|
| | if TYPE_CHECKING: |
| | from swift.llm.infer import Function |
| | from swift.llm.template import Prompt |
| |
|
| |
|
| | class GLM4AgentTemplate(BaseAgentTemplate): |
| | is_glm4_0414 = False |
| |
|
| | @staticmethod |
| | def _find_function_call(single_content: str) -> Optional['Function']: |
| | from swift.llm.infer import Function |
| | single_content = single_content.replace('<|observation|>', '') |
| | pattern = re.compile(r'([^\n`]*?)\n({.*?})(?=\w*\n|$)', re.DOTALL) |
| | matches = pattern.findall(single_content) |
| | if not matches: |
| | return |
| |
|
| | name, arguments = matches[0] |
| | return Function(name=name, arguments=arguments) |
| |
|
| | def get_toolcall(self, response: str) -> List['Function']: |
| | toolcall_list = response.split('<|assistant|>') |
| | functions = [] |
| | for toolcall in toolcall_list: |
| | function = self._find_function_call(toolcall) |
| | if function: |
| | functions.append(function) |
| | if len(functions) == 0: |
| | |
| | return super().get_toolcall(response) |
| | return functions |
| |
|
| | def _format_tools(self, tools: List[Union[str, dict]], system: str, user_message=None) -> str: |
| | tool_descs = [] |
| | for tool in tools: |
| | tool = self.unwrap_tool(tool) |
| | name = self._get_tool_name(tool) |
| | tool_descs.append(f'## {name}\n\n{json.dumps(tool, ensure_ascii=False, indent=4)}\n' |
| | '在调用上述函数时，请使用 Json 格式表示调用的参数。') |
| | glm4_system = '你是一个名为 GLM-4 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n' |
| | return ('' if self.is_glm4_0414 else glm4_system) + """# 可用工具 |
| | |
| | """ + '\n'.join(tool_descs) |
| |
|
| | def _format_tool_responses( |
| | self, |
| | assistant_content: str, |
| | tool_messages, |
| | ) -> Tuple[str, 'Prompt']: |
| | with_action = self.keyword.action in assistant_content and self.keyword.action_input in assistant_content |
| | if with_action: |
| | return super()._format_tool_responses(assistant_content, tool_messages) |
| | res = ['\n'] |
| | for i, tool_message in enumerate(tool_messages): |
| | tool_content = tool_message['content'] |
| | if i > 0: |
| | res.append('<|observation|>\n') |
| | res.append(tool_content) |
| | res.append('<|assistant|>\n') |
| | return assistant_content, res |
| |
|
| | def _format_tool_calls(self, tool_call_messages) -> str: |
| | tool_calls = [] |
| | for message in tool_call_messages: |
| | tool_call = self._parse_tool_call(message['content']) |
| | tool_calls.append(f'{tool_call["name"]}\n{tool_call["arguments"]}') |
| | return '<|assistant|>'.join(tool_calls) + '<|observation|>' |
| |
|
| |
|
| | class GLM4_0414AgentTemplate(GLM4AgentTemplate): |
| | is_glm4_0414 = True |
| |
|
