| | |
| | import re |
| | from typing import TYPE_CHECKING, List, Tuple, Union |
| |
|
| | import json |
| |
|
| | from .base import BaseAgentTemplate |
| |
|
| | if TYPE_CHECKING: |
| | from swift.llm.infer import Function |
| | from swift.llm.template import Prompt |
| |
|
| |
|
| | class Llama3AgentTemplate(BaseAgentTemplate): |
| | eom_token = '<|eom_id|>' |
| | start_token = '<|start_header_id|>' |
| | end_token = '<|end_header_id|>' |
| | eot_token = '<|eot_id|>' |
| |
|
| | def get_toolcall(self, response: str) -> List['Function']: |
| | from swift.llm.infer import Function |
| | if response.endswith(self.eom_token): |
| | response = response[:-len(self.eom_token)] |
| | functions = [] |
| | res_list = re.findall(r'{[^{]*?"name":.*?"parameters":\s*?{.*?}\s*?}', response, re.DOTALL) |
| | for res in res_list: |
| | res = self._parse_json(res) |
| | if isinstance(res, dict) and 'name' in res and 'parameters' in res: |
| | functions.append(Function(name=res['name'], arguments=res['parameters'])) |
| | if len(functions) == 0: |
| | |
| | return super().get_toolcall(response) |
| | return functions |
| |
|
| | 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 = [self.eot_token] |
| | for tool_message in tool_messages: |
| | tool_content = tool_message['content'] |
| | res.append(f'{self.start_token}tool{self.end_token}\n\n{tool_content}{self.eot_token}') |
| | res.append(f'{self.start_token}assistant{self.end_token}\n\n') |
| | return assistant_content, res |
| |
|
| | def _format_tools(self, tools: List[Union[str, dict]], system: str, user_message=None) -> str: |
| | assert user_message is not None |
| | user_content = user_message['content'] |
| | tool_descs = [json.dumps(tool, ensure_ascii=False, indent=4) for tool in tools] |
| | new_user_content = """Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt. |
| | |
| | Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables. |
| | |
| | """ + '\n\n'.join(tool_descs) + f""" |
| | |
| | {user_content}""" |
| | user_message['content'] = new_user_content |
| | return system |
| |
|
| | 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_call['parameters'] = tool_call.pop('arguments') |
| | tool_calls.append(json.dumps(tool_call, ensure_ascii=False)) |
| | return '\n'.join(tool_calls) |
| |
|
| |
|
| | class Llama4AgentTemplate(Llama3AgentTemplate): |
| | eom_token = '<|eom|>' |
| | start_token = '<|header_start|>' |
| | end_token = '<|header_end|>' |
| | eot_token = '<|eot|>' |
| | toolcall_pattern = r'(.+?)<\|eom\|>' |
| |
|
