For the base tree search method, you need to support the following functions
| 1 | from Downstream_tasks.base_env import base_env |
| 2 | |
| 3 | class base_search_method: |
| 4 | """For the base tree search method, you need to support the following functions""" |
| 5 | |
| 6 | def __init__(self,llm,io_func: base_env, process_id=0, callbacks = None): |
| 7 | """Args: |
| 8 | llm: The interface of the LLM |
| 9 | io_func(base_env): Interface to the environment, |
| 10 | process_id (int, optional): In multiprocessing annotation, this describes the process id. Defaults to 0. |
| 11 | callbacks (_type_, optional): _description_. Defaults to None. |
| 12 | """ |
| 13 | pass |
| 14 | |
| 15 | def to_json(self,answer=False,process=True): |
| 16 | ''' |
| 17 | return a json object, |
| 18 | If "answer" = True. must have the following field to make answer annotation |
| 19 | If "process" = True. You need provide the full information of the tree searching process |
| 20 | |
| 21 | "answer_generation": { |
| 22 | "valid_data": bool, |
| 23 | "final_answer": string, |
| 24 | "finish_type": enum["give_up","give_answer"] |
| 25 | "train_messages": [ [openAI-message] ], |
| 26 | } |
| 27 | ''' |
| 28 | raise NotImplementedError |
| 29 | |
| 30 | def start(self, **args): |
| 31 | """This is the entry point of the searching process""" |
| 32 | raise NotImplementedError |
| 33 |
nothing calls this directly
no outgoing calls
no test coverage detected