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import logging
import os
import time
import openai
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class OpenAI:
def __init__(
self,
api_key,
strategy="cot",
evaluation_strategy="value",
api_base="",
api_model="",
):
if api_key == "" or api_key is None:
api_key = os.environ.get("OPENAI_API_KEY", "")
if api_key != "":
openai.api_key = api_key
else:
raise Exception("Please provide OpenAI API key")
if api_base == ""or api_base is None:
api_base = os.environ.get("OPENAI_API_BASE", "") # if not set, use the default base path of "https://api.openai.com/v1"
if api_base != "":
# e.g. https://api.openai.com/v1/ or your custom url
openai.api_base = api_base
print(f'Using custom api_base {api_base}')
if api_model == "" or api_model is None:
api_model = os.environ.get("OPENAI_API_MODEL", "")
if api_model != "":
self.api_model = api_model
else:
self.api_model = "text-davinci-003"
print(f'Using api_model {self.api_model}')
self.use_chat_api = 'gpt' in self.api_model
self.strategy = strategy
self.evaluation_strategy = evaluation_strategy
def run(
self,
prompt,
max_tokens,
temperature,
k=1,
stop=None
):
while True:
try:
if self.use_chat_api:
messages = [
{
"role": "user",
"content": prompt
}
]
response = openai.ChatCompletion.create(
model=self.api_model,
messages=messages,
max_tokens=max_tokens,
temperature=temperature,
)
else:
response = openai.Completion.create(
engine=self.api_model,
prompt=prompt,
n=k,
max_tokens=max_tokens,
stop=stop,
temperature=temperature,
)
with open("openai.logs", 'a') as log_file:
log_file.write("\n" + "-----------" + '\n' +"Prompt : "+ prompt+"\n")
return response
except openai.error.RateLimitError as e:
sleep_duratoin = os.environ.get("OPENAI_RATE_TIMEOUT", 30)
print(f'{str(e)}, sleep for {sleep_duratoin}s, set it by env OPENAI_RATE_TIMEOUT')
time.sleep(sleep_duratoin)
def openai_choice2text_handler(self, choice):
if self.use_chat_api:
text = choice['message']['content']
else:
text = choice.text.strip()
return text
def generate_text(self, prompt, k):
if self.use_chat_api:
thoughts = []
for _ in range(k):
response = self.run(prompt, 400, 0.5, k)
text = self.openai_choice2text_handler(response.choices[0])
thoughts += [text]
# print(f'thoughts: {thoughts}')
return thoughts
else:
response = self.run(prompt, 300, 0.5, k)
thoughts = [self.openai_choice2text_handler(choice) for choice in response.choices]
return thoughts
def generate_thoughts(
self,
state,
k,
initial_prompt,
rejected_solutions=None
):
if (type(state) == str):
state_text = state
else:
state_text = '\n'.join(state)
print("New state generating thought:", state, "\n\n")
prompt = f"""
Accomplish the task below by decomposing it as many very explicit subtasks as possible, be very explicit and thorough denoted by
a search process, highlighted by markers ‘1’,..., ‘3’ as “first operations” guiding subtree exploration for the OBJECTIVE,
focus on the third subtree exploration. Produce prospective search steps (e.g., the subtree exploration ‘5. 11 + 1’)
and evaluates potential subsequent steps to either progress
towards a solution or retrace to another viable subtree then be very thorough
and think atomically then provide solutions for those subtasks,
then return the definitive end result and then summarize it
########## OBJECTIVE
{initial_prompt}
###################
"""
thoughts = self.generate_text(prompt, k)
# print(f"Generated thoughts: {thoughts}")
return thoughts
def generate_solution(self,
initial_prompt,
state,
rejected_solutions=None):
try:
if isinstance(state, list):
state_text = '\n'.join(state)
else:
state_text = state
prompt = f"""
Generate a series of solutions to comply with the user's instructions,
you must generate solutions on the basis of determining the most reliable solution in the shortest amount of time,
while taking rejected solutions into account and learning from them.
Considering the reasoning provided:\n\n
###'{state_text}'\n\n###
Devise the best possible solution for the task: {initial_prompt}, Here are evaluated solutions that were rejected:
###{rejected_solutions}###,
complete the {initial_prompt} without making the same mistakes you did with the evaluated rejected solutions. Be simple. Be direct. Provide intuitive solutions as soon as you think of them."""
answer = self.generate_text(prompt, 1)
print(f'Generated Solution Summary {answer}')
return answer
except Exception as e:
logger.error(f"Error in generate_solutions: {e}")
return None
def evaluate_states(self, states, initial_prompt):
if not states:
return {}
if self.evaluation_strategy == 'value':
state_values = {}
for state in states:
if (type(state) == str):
state_text = state
else:
state_text = '\n'.join(state)
print("We receive a state of type", type(state), "For state: ", state, "\n\n")
prompt = f""" To achieve the following goal: '{initial_prompt}', pessimistically value the context of the past solutions and more importantly the latest generated solution you had AS A FLOAT BETWEEN 0 AND 1\n
Past solutions:\n\n
{state_text}\n
If the solutions is not making fast progress in achieving the goal, give it a lower score.
Evaluate all solutions AS A FLOAT BETWEEN 0 and 1:\n, DO NOT RETURN ANYTHING ELSE
"""
response = self.run(prompt, 10, 1)
try:
value_text = self.openai_choice2text_handler(response.choices[0])
# print(f'state: {value_text}')
value = float(value_text)
print(f"Evaluated Thought Value: {value}")
except ValueError:
value = 0
state_values[state] = value
return state_values
else:
raise ValueError("Invalid evaluation strategy. Choose 'value' or 'vote'.")
class AoTAgent:
def __init__(
self,
num_thoughts: int = None,
max_steps: int = None,
value_threshold: float = None,
pruning_threshold=0.5,
backtracking_threshold=0.4,
initial_prompt=None,
openai_api_key: str = None,
model = None,
):
self.num_thoughts = num_thoughts
self.max_steps = max_steps
self.value_threshold = value_threshold
self.backtracking_threshold = backtracking_threshold
self.pruning_threshold = pruning_threshold
self.initial_prompt = initial_prompt
self.output = []
self.openai_api_key = openai_api_key
self.model = model
self.model = self.model or OpenAI(api_key=self.openai_api_key)
def solve(self):
try:
self.dfs(self.initial_prompt, 1)
if not self.output:
logger.error("No valid thoughts were generated during DFS")
return None
best_state, _ = max(self.output, key=lambda x: x[1])
solution = self.model.generate_solution(self.initial_prompt, best_state)
print(f"Solution is {solution}")
return solution if solution else best_state
except Exception as error:
logger.error(f"Error in tot_dfs: {error}")
raise error
def dfs(self, state, step):
if step > self.max_steps:
thought, value = self.evaluate_thought(state)
self.output.append((thought, value))
return
thoughts = self.generate_and_filter_thoughts(state)
for next_state in thoughts:
state_value = self.evaluated_thoughts[next_state]
if state_value > self.value_threshold:
child = (state, next_state) if isinstance(state, str) else (*state, next_state)
self.dfs(child, step + 1)
#backtracking
best_value = max([value for _, value in self.output])
if best_value < self.backtracking_threshold:
self.output.pop()
continue
def generate_and_filter_thoughts(self, state):
thoughts = self.model.generate_thoughts(
state,
self.num_thoughts,
self.initial_prompt
)
self.evaluated_thoughts = self.model.evaluate_states(
thoughts,
self.initial_prompt
)
filtered_thoughts = [thought for thought in thoughts if self.evaluated_thoughts[thought] >= self.pruning_threshold]
print(f"filtered_thoughts: {filtered_thoughts}")
return filtered_thoughts
def evaluate_thought(self, state):
thought = self.model.generate_thoughts(state, 1, self.initial_prompt)
value = self.model.evaluate_states([state], self.initial_prompt)[state]
print(f"Evaluated thought: {value}")
return thought, value |