199 lines
8.4 KiB
Python
199 lines
8.4 KiB
Python
"""
|
|
Utils for tooleval.
|
|
"""
|
|
from toolbench.tooleval.evaluation import ExecutionGraph,ExecutionNode
|
|
import random
|
|
random.seed(42)
|
|
from toolbench.tooleval.evaluators.registered_cls.rtl import AnswerStatus, TaskStatus
|
|
|
|
task_status_mapping = {
|
|
"TaskStatus.Solvable": TaskStatus.Solvable,
|
|
"TaskStatus.Unsolvable": TaskStatus.Unsolvable,
|
|
"TaskStatus.Unsure": TaskStatus.Unsure
|
|
}
|
|
answer_status_mapping = {
|
|
"AnswerStatus.Solved": AnswerStatus.Solved,
|
|
"AnswerStatus.Unsolved": AnswerStatus.Unsolved,
|
|
"AnswerStatus.Unsure": AnswerStatus.Unsure
|
|
}
|
|
# test_sets = ["G1_category"]
|
|
test_sets = ["G1_instruction", "G1_category", "G1_tool", "G2_instruction", "G2_category", "G3_instruction"]
|
|
|
|
def get_steps(example):
|
|
answer_details = example["answer"]["answer_details"][0]
|
|
answer_steps = []
|
|
step_cnt = 1
|
|
final_step = ""
|
|
|
|
while "next" in answer_details:
|
|
answer_str = answer_details["message"]
|
|
role_str = answer_details["role"]
|
|
|
|
if answer_str and role_str == "tool":
|
|
step_text = f"Step {step_cnt}: {answer_str}"
|
|
answer_steps.append(step_text)
|
|
final_step = f"Final step: {answer_str}"
|
|
step_cnt += 1
|
|
|
|
if not answer_details["next"]:
|
|
break
|
|
|
|
answer_details = answer_details["next"][0]
|
|
|
|
return "\n".join(answer_steps), final_step
|
|
|
|
|
|
def generate_init_message_node(eg:ExecutionGraph,functions,query):
|
|
init_node = ExecutionNode(role='system', message="You are AutoGPT, you can use many tools(functions) to do the following task.\nFirst I will give you the task description, and your task start.\nAt each step, you need to give your thought to analyze the status now and what to do next, with a function call to actually excute your step.\nAfter the call, you will get the call result, and you are now in a new state.\nThen you will analyze your status now, then decide what to do next...\nAfter many (Thought-call) pairs, you finally perform the task, then you can give your finial answer.\nRemember: \n1.the state change is irreversible, you can't go back to one of the former state, if you want to restart the task, say \"I give up and restart\".\n2.All the thought is short, at most in 5 sentence.\n3.You can do more then one trys, so if your plan is to continusly try some conditions, you can do one of the conditions per try.\nLet's Begin!\nTask description: You should use functions to help handle the real time user querys. Remember to ALWAYS call \"Finish\" function at the end of the task. And the final answer should contain enough information to show to the user.\nSpecifically, you have access to the following functions: " + str(functions))
|
|
eg.set_init_node(init_node)
|
|
|
|
node = ExecutionNode(role='user', message=query)
|
|
eg.add_node(node)
|
|
eg[init_node,node] = None
|
|
return node
|
|
|
|
def process_valid_data(method,answer_generation):
|
|
conversation = answer_generation['train_messages'][-1]
|
|
functions = answer_generation['function']
|
|
query = answer_generation['query']
|
|
eg = ExecutionGraph()
|
|
last_node = generate_init_message_node(eg,functions,query)
|
|
|
|
index = 2
|
|
while index < len(conversation):
|
|
message = conversation[index]
|
|
role = message['role']
|
|
if role == 'system' or role == 'user' or role == 'function':
|
|
index = index + 1
|
|
continue
|
|
elif role == 'assistant':
|
|
if 'function_call' in message :
|
|
node = ExecutionNode(role='tool', message={
|
|
'name':message['function_call']['name'],
|
|
'arguments':message['function_call']['arguments'],
|
|
'response':conversation[index+1]['content'] if message['function_call']['name']!='Finish' else ''
|
|
})
|
|
index = index + 1
|
|
else:
|
|
node = ExecutionNode(role='assistant',
|
|
message=message['content'])
|
|
|
|
else:
|
|
raise NotImplementedError(f'Unkown role {role}')
|
|
|
|
index = index + 1
|
|
eg.add_node(node)
|
|
eg[last_node,node] = None
|
|
last_node = node
|
|
|
|
eg = eg.reduce_graph_to_sequence()
|
|
|
|
return {
|
|
'query':query,
|
|
'available_tools':functions,
|
|
'answer':{
|
|
'method':method,
|
|
'total_steps': eg.node_count,
|
|
'final_answer': answer_generation['final_answer'],
|
|
'answer_details': eg.convert_to_dict()
|
|
}
|
|
}
|
|
|
|
def process_invalid_data(method,data_dict):
|
|
answer_generation = data_dict['answer_generation']
|
|
functions = answer_generation['function']
|
|
query = answer_generation['query']
|
|
eg = ExecutionGraph()
|
|
last_node = generate_init_message_node(eg,functions,query)
|
|
if 'CoT' in method:
|
|
trail = random.choice(data_dict["trys"])
|
|
index = 0
|
|
while index < len(trail['chain']):
|
|
message = trail['chain'][index]
|
|
if message['node_type'] == 'Action':
|
|
node = ExecutionNode(role='tool', message={
|
|
'name':message['description'],
|
|
'arguments':(trail['chain'][index+1]['description']),
|
|
'response':(trail['chain'][index+1]['observation'])})
|
|
|
|
index = index + 1
|
|
elif message['node_type'] == 'Thought':
|
|
node = ExecutionNode(role='assistant',
|
|
message=message['description'])
|
|
else:
|
|
raise NotImplementedError(f"Unknown node_type: {message['node_type']}")
|
|
index = index + 1
|
|
|
|
eg.add_node(node)
|
|
eg[last_node,node] = None
|
|
last_node = node
|
|
eg = eg.reduce_graph_to_sequence()
|
|
|
|
elif 'DFS' in method:
|
|
|
|
def DFS(root):
|
|
if len(root['children']) == 0:
|
|
node = ExecutionNode(role=root['node_type'],message=root)
|
|
eg.add_node(node)
|
|
return node
|
|
else:
|
|
child_nodes = [DFS(node) for node in root['children']]
|
|
root['children'] = None
|
|
root_node = ExecutionNode(role=root['node_type'],message=root)
|
|
eg.add_node(root_node)
|
|
for child_node in child_nodes:
|
|
eg.add_edge(root_node,child_node)
|
|
return root_node
|
|
for node in data_dict['tree']['tree']['children']:
|
|
eg[last_node,DFS(node)] = None
|
|
|
|
|
|
# purify the graph
|
|
def purify_graph(node:ExecutionNode):
|
|
if node.role == 'Action':
|
|
adj_nodes = eg.get_adjacent_node(node)
|
|
for adj_node in adj_nodes:
|
|
adj_node = eg[adj_node]
|
|
if adj_node.role == 'Action Input':
|
|
node.role = 'tool'
|
|
node.message = {
|
|
'name':node.message['description'],
|
|
'arguments':(adj_node.message['description']),
|
|
'response':(adj_node.message['observation'])
|
|
|
|
}
|
|
# remove adj_node
|
|
adj_node = eg.pop_node(adj_node)
|
|
to_nodes = eg.edges.pop(adj_node.node_id,{})
|
|
eg.edges[node.node_id].update(to_nodes)
|
|
eg.edges[node.node_id].pop(adj_node.node_id)
|
|
node.out_degree += len(to_nodes)
|
|
break
|
|
elif node.role == 'Thought':
|
|
node.role = 'assistant'
|
|
node.message = node.message['description']
|
|
elif node.role == 'Action Input':
|
|
print('Founding Extra Action Input Node')
|
|
pass
|
|
elif node.role =='system' or node.role=='user':
|
|
pass
|
|
else:
|
|
raise Exception('Unknown role {}'.format(node.role))
|
|
adj_nodes = eg.get_adjacent_node(node)
|
|
for adj_node in adj_nodes:
|
|
purify_graph(eg[adj_node])
|
|
|
|
purify_graph(last_node)
|
|
eg = eg.reduce_graph_to_sequence()
|
|
else:
|
|
raise NotImplementedError(f'Unknown method {method}')
|
|
return {
|
|
'query':query,
|
|
'available_tools':functions,
|
|
'answer':{
|
|
'method':method,
|
|
'total_steps': eg.node_count,
|
|
'final_answer': answer_generation['final_answer'],
|
|
'answer_details': eg.convert_to_dict()
|
|
}
|
|
}
|