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| import json | |
| from typing import Dict, Iterator, List, Optional | |
| from qwen_agent.actions.base import Action | |
| from qwen_agent.tools import call_plugin | |
| TOOL_DESC = """{name_for_model}: Call this tool to interact with the {name_for_human} API. What is the {name_for_human} API useful for? {description_for_model} Parameters: {parameters}""" | |
| PROMPT_REACT = """Answer the following questions as best you can. You have access to the following tools: | |
| {tool_descs} | |
| Use the following format: | |
| Question: the input question you must answer | |
| Thought: you should always think about what to do | |
| Action: the action to take, should be one of [{tool_names}] | |
| Action Input: the input to the action | |
| Observation: the result of the action | |
| (this Thought/Action/Action Input/Observation can be repeated zero or more times) | |
| Thought: I now know the final answer | |
| Final Answer: the final answer to the original input question | |
| Begin! | |
| Question: {query}""" | |
| def _build_react_instruction(query: str, functions: List[Dict]): | |
| tool_descs = [] | |
| tool_names = [] | |
| for info in functions: | |
| tool_descs.append( | |
| TOOL_DESC.format( | |
| name_for_model=info['name_for_model'], | |
| name_for_human=info['name_for_human'], | |
| description_for_model=info['description_for_model'], | |
| parameters=json.dumps(info['parameters'], ensure_ascii=False), | |
| )) | |
| tool_names.append(info['name_for_model']) | |
| tool_descs = '\n\n'.join(tool_descs) | |
| tool_names = ','.join(tool_names) | |
| prompt = PROMPT_REACT.format(tool_descs=tool_descs, | |
| tool_names=tool_names, | |
| query=query) | |
| return prompt | |
| def _parse_last_action(text): | |
| plugin_name, plugin_args = '', '' | |
| i = text.rfind('\nAction:') | |
| j = text.rfind('\nAction Input:') | |
| k = text.rfind('\nObservation:') | |
| if 0 <= i < j: # If the text has `Action` and `Action input`, | |
| if k < j: # but does not contain `Observation`, | |
| # then it is likely that `Observation` is ommited by the LLM, | |
| # because the output text may have discarded the stop word. | |
| text = text.rstrip() + '\nObservation:' # Add it back. | |
| k = text.rfind('\nObservation:') | |
| plugin_name = text[i + len('\nAction:'):j].strip() | |
| plugin_args = text[j + len('\nAction Input:'):k].strip() | |
| text = text[:k] # Discard '\nObservation:'. | |
| return plugin_name, plugin_args, text | |
| # TODO: When to put an parameter (such as history) in __init__()? When to put it in run()? | |
| class ReAct(Action): | |
| def _run(self, | |
| user_request, | |
| functions: List[Dict] = None, | |
| history: Optional[List[Dict]] = None, | |
| lang: str = 'en') -> Iterator[str]: | |
| functions = functions or [] | |
| prompt = _build_react_instruction(user_request, functions) | |
| messages = [] | |
| if history: | |
| assert history[-1][ | |
| 'role'] != 'user', 'The history should not include the latest user query.' | |
| messages.extend(history) | |
| messages.append({'role': 'user', 'content': prompt}) | |
| max_turn = 5 | |
| while True and max_turn > 0: | |
| max_turn -= 1 | |
| output = self.llm.chat( | |
| messages=messages, | |
| stream=False, # TODO: | |
| stop=['Observation:', 'Observation:\n'], | |
| ) | |
| action, action_input, output = _parse_last_action(output) | |
| if messages[-1]['content'].endswith('\nThought:'): | |
| if not output.startswith(' '): | |
| output = ' ' + output | |
| else: | |
| if not output.startswith('\n'): | |
| output = '\n' + output | |
| yield output | |
| if action: | |
| observation = call_plugin(action, action_input) | |
| observation = f'\nObservation: {observation}\nThought:' | |
| yield observation | |
| messages[-1]['content'] += output + observation | |
| else: | |
| break | |